Happening @ Michigan https://events.umich.edu/list/rss RSS Feed for Happening @ Michigan Events at the University of Michigan. LHS Collaboratory Webinar - Global LHS for COVID-19 (July 29, 2020 10:00am) https://events.umich.edu/event/75087 75087-19214577@events.umich.edu Event Begins: Wednesday, July 29, 2020 10:00am
Location: Off Campus Location
Organized By: Department of Learning Health Sciences

Please join us for this special webinar session on Wednesday, July 29, 2020 from 10:00 am - 11:30 pm EDT.  Registration: https://umich-health.zoom.us/webinar/register/WN_wVDWLBm5QYK79DVK8Tb7_w

This 90-minute webinar is designed to share the work of an international collaboration to develop the foundation for a global Learning Health System addressing COVID-19 and future public health crises. Presenters will share lessons learned from Italy, Spain and the United States, including describing a proposed international comprehensive systemic framework for collection, management, and
analysis of high-quality data to inform decisions in managing the clinical response and social measures to overcome the COVID-19 pandemic. Additionally, presenters will discuss how the results of a pilot project currently under development may illuminate a collaborative path forward for local, regional, and national public health stakeholders worldwide.

Perspectives from Italy:  Paolo Stocco

Perspectives from Spain: Borja Sanchez Garcia, Pablo Rivero, Francisco Ros, Esther Gil Zorzo 

Perspectives from the USA: Charles P. Friedman, Rebecca Kush, Joshua C. Rubin, Douglas Van Houweling

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Lecture / Discussion Mon, 29 Jun 2020 15:22:14 -0400 2020-07-29T10:00:00-04:00 2020-07-29T11:30:00-04:00 Off Campus Location Department of Learning Health Sciences Lecture / Discussion LHS Collaboratory Logo-globe
DCMB / CCMB Weekly Seminar Series (September 9, 2020 4:00pm) https://events.umich.edu/event/76946 76946-19780535@events.umich.edu Event Begins: Wednesday, September 9, 2020 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract: Birth defects that interfere with craniofacial development can result in cognitive, neurosensory, and neuroendocrine defects that create life-long burdens for care. The forebrain, midbrain, hindbrain, five facial prominences, and pituitary gland develop between the first and second month of gestation in humans. Genetic defects that disrupt these processes cause a spectrum of disorders that range from holoprosencephaly (HPE) and septo-optic dysplasia (SOD) to pituitary hormone deficiencies. We screened a large cohort of Argentinean patients with congenital hypopituitarism and related disorders for mutations in known genes and identified novel pathogenic variants and examples of digenic disease. However, the majority of patients did not receive a molecular diagnosis, indicating the high degree of genetic complexity underlying these disorders and the need for additional gene discovery. The majority of known hypopituitarism genes were discovered through basic research in pituitary cell lines and mutant mice. To identify novel regulatory genes for pituitary organogenesis we analyzed differential binding of a key pituitary-specific transcription factor, POU1F1, in cell lines that represent pituitary progenitors and differentiated cells. We discovered that POU1F1 binding is associated with bZIP transcription factors in progenitors and with bHLH factors in differentiated cells. We also applied single cell RNA sequencing technology to analyze gene expression during pituitary organogenesis and discovered novel transcription factors that are candidates for driving cell specification as well as unique, rare cell types that are likely differentiation intermediates. Bioinformatic analyses have played key roles in advancing our knowledge of neuroendocrine birth defects and normal pituitary organogenesis.

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Lecture / Discussion Wed, 09 Sep 2020 08:26:42 -0400 2020-09-09T16:00:00-04:00 2020-09-09T17:00:00-04:00 Off Campus Location DCMB Seminar Series Lecture / Discussion Sally Camper, Ph.D., Margery Shaw Distinguished University Professor of Human Genetics, Professor of Internal Medicine, University of Michigan
LHS Collaboratory Seminar Series Virtual Kick-Off: Academic Medical Centers as Learning Health Systems (September 17, 2020 9:00am) https://events.umich.edu/event/75856 75856-19615923@events.umich.edu Event Begins: Thursday, September 17, 2020 9:00am
Location: Off Campus Location
Organized By: Department of Learning Health Sciences

Learning Health Systems (LHS) methods are now being implemented in interesting and varying ways by academic health centers and their clinical and translational science institutes across the country.
According to the Agency for Healthcare Research and Quality (AHRQ), the following are key attributes of Learning Health
Systems:

• Having leaders who are committed to a culture of continuous learning and improvement
• Systematically gathering and applying evidence in real-time to guide care
• Employing IT methods to share new evidence with clinicians to improve decision-making
• Promoting the inclusion of patients as vital members of the learning team
• Capturing and analyzing data and care experiences to improve care
• Continually assessing outcomes, refining processes and training to create a feedback cycle for learning and improvement

The LHS Collaboratory's fall seminar series virtual kick-off event will showcase the LHS experiences of three research-intensive academic centers that have been promoting LHS methods. We will be joined by distinguished senior colleagues from Duke,Vanderbilt, and Washington University, who will describe and discuss their institutions' work in this area. They will discuss strategies employed, investments made, challenges encountered, and successes achieved.

Panelists:
Kevin B. Johnson, MD, MS, FAAP, FACMI, Vanderbilt University
Christopher J. Lindsell, PhD, Vanderbilt University
Philip Payne, PhD, FACMI, Washington University
Michael Pencina, PhD, Duke University
Eric G. Poon, MD, MPH, Duke University

Discussant:
Carol R. Bradford, MD, MS, Executive Vice Dean for Academic Affairs, University of Michigan Medical School, Chief Academic Officer, Michigan Medicine, Professor of Otolaryngology-Head and Neck Surgery

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Lecture / Discussion Thu, 20 Aug 2020 09:45:31 -0400 2020-09-17T09:00:00-04:00 2020-09-17T11:00:00-04:00 Off Campus Location Department of Learning Health Sciences Lecture / Discussion LHS Collaboratory Logo-blocks
DCMB / CCMB Weekly Virtual Seminar featuring Gioele La Manno, Ph.D. (EPFL Life Sciences Early Independent Research Scholar (ELISIR) (September 18, 2020 12:00pm) https://events.umich.edu/event/77057 77057-19836073@events.umich.edu Event Begins: Friday, September 18, 2020 12:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

I will present our comprehensive single-cell transcriptome atlas of mouse brain development spanning from gastrulation to birth. In this atlasing effort, we identified almost a thousand distinct cellular states, including the initial emergence of the neuroepithelium, different glioblasts, and a rich set of region-specific secondary organizers that we localize spatially. In this context, I will provide an example of how the spatially-resolved transcriptomic data can be particularly useful to interpret the complexity of such complex atlases.

Continuing in this direction, I will show the approach that we recently proposed as a general way to spatially resolve different types of next-generation sequencing data. We designed an imaging-free framework to localize high throughput readouts within a tissue by combining compressive sampling and image reconstruction. Our first implementation of this framework transformed a low-input RNA sequencing protocol into an imaging-free spatial transcriptomics technique (STRP-seq).

Finally, I will showcase the technique with the profiling of the brain of the Australian bearded dragon Pogona vitticeps. With this analysis, we revealed the molecular anatomy of the telencephalon of this lizard and provided evidence for a marked regionalization of the reptilian pallium and subpallium.

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Lecture / Discussion Wed, 16 Sep 2020 11:27:53 -0400 2020-09-18T12:00:00-04:00 2020-09-18T13:00:00-04:00 Off Campus Location DCMB Seminar Series Lecture / Discussion Gioele La Manno, Ph.D. (EPFL Life Sciences Early Independent Research Scholar (ELISIR) École polytechnique fédérale de Lausanne ‐ EPFL Swiss Federal Institute of Technology Lausanne)
DCMB / CCMB Weekly Virtual Seminar (September 23, 2020 4:00pm) https://events.umich.edu/event/77143 77143-19798542@events.umich.edu Event Begins: Wednesday, September 23, 2020 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Talk title: Decision Support System Applications in Dentistry

Dr. Lucia Cevidanes is the Thomas and Doris Graber Professor of Dentistry and Associate Professor at the Department of Orthodontics at the University of Michigan, and a Diplomate of the American Board of Orthodontics. She is a practicing clinician who has published over 150 manuscripts on 3D imaging for which she has received research grants from the American Association of Orthodontics Foundation and the National Institute of Dental and Craniofacial Research. Her work has been recognized by the American Association of Orthodontists Thomas M. Graber Award, the B F Dewel Award, Milo Hellman Award, and the Wuehrmann award from the American Academy of Oral and Maxillofacial Radiology. Her interests include Artificial Intelligence and 3D Imaging to solve difficult clinical problems in dentistry, studying current and new treatment approaches and technical procedures, and understanding treatment outcomes for craniofacial anomalies and dentofacial deformities.

Zoom Link: https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Lecture / Discussion Fri, 11 Sep 2020 15:27:53 -0400 2020-09-23T16:00:00-04:00 2020-09-23T17:00:00-04:00 Off Campus Location DCMB Seminar Series Lecture / Discussion Dr. Lucia Cevidanes is the Thomas and Doris Graber Professor of Dentistry and Associate Professor at the Department of Orthodontics at the University of Michigan
DCMB / CCMB Weekly Virtual Seminar - Xiaotian Zhang, Ph.D. (September 30, 2020 4:00pm) https://events.umich.edu/event/77549 77549-19883820@events.umich.edu Event Begins: Wednesday, September 30, 2020 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract: The human genome is organized into small compartments to allow for the proper gene expression regulation in the physiological process. With the advance of next-generation sequencing and imaging technologies, we can now investigate how the genome is folded into 3D space and how the 3D genomic organization regulates gene expression in development and disease. Currently, most of the studies are focusing on CTCF and cohesion complex which partner together to facilitate the formation of topological associated domains (TAD). The presenter will mainly discuss his recently published work on the DNA methylation -3D genomics cross-talk. Unpublished work on the 3D genomics in AML will be discussed as well.

Short bio: Xiaotian Zhang obtained his Ph.D. at Baylor College of Medicine with Dr. Margaret Goodell on the role of DNA methylation synergy in leukemia development. He was previously the Van Andel special postdoc fellow in Gerd Pfeifer lab working on the 3D genomics in normal hematopoietic stem cell and leukemia. He is now a Research track faculty (Research Investigator) in Pathology Department under Tomek Cierpicki working on the HOXA regulation in leukemia development. Xiaotian's research focuses on the epigenetic regulation of key pathogenic genes in leukemia, particularly on high order chromatin structure in disease. He published on Nature Genetics, Molecular Cell and Blood as the first author and corresponding authors.

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Lecture / Discussion Tue, 22 Sep 2020 09:31:31 -0400 2020-09-30T16:00:00-04:00 2020-09-30T17:00:00-04:00 Off Campus Location DCMB Seminar Series Lecture / Discussion Xiaotian Zhang, Ph.D., Research Investigator in the Department of Pathology at the University of Michigan
DCMB / CCMB Weekly Seminar (October 7, 2020 4:00pm) https://events.umich.edu/event/78232 78232-19996937@events.umich.edu Event Begins: Wednesday, October 7, 2020 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract: The chromosomes of the human genome are organized in three-dimensions by compartmentalizing the cell nucleus and different genomic loci also interact with each other. However, the principles underlying such nuclear genome organization and its functional impact remain poorly understood. In this talk, I will introduce some of our recent work in developing machine learning methods by utilizing whole-genome mapping data to study the higher-order genome organization. Our methods reveal the spatial localization of chromosome regions and exploit chromatin interactome patterns within the cell nucleus in different cellular conditions, across mammalian species, and also in single-cell resolution. We hope that these algorithms will provide new insights into the principles of nuclear spatial organization.

Bio: Jian Ma is an Associate Professor in the Computational Biology Department within the School of Computer Science at Carnegie Mellon University. He was previously on the faculty of the University of Illinois at Urbana-Champaign. His lab develops algorithms to study the structure and function of the human genome with a focus on nuclear organization, gene regulation, comparative genomics, and single cell biology. He received several awards, including an NSF CAREER award and a Guggenheim Fellowship. He is the Contact PI of a UM1 Center project in the NIH 4D Nucleome Program (Phase 2; 2020-2025). https://www.cs.cmu.edu/~jianma/

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Livestream / Virtual Tue, 06 Oct 2020 12:47:39 -0400 2020-10-07T16:00:00-04:00 2020-10-07T17:00:00-04:00 Off Campus Location DCMB Seminar Series Livestream / Virtual
DCMB / CCMB Weekly Seminar (October 14, 2020 4:00pm) https://events.umich.edu/event/78234 78234-19996940@events.umich.edu Event Begins: Wednesday, October 14, 2020 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract: Gaussian processes provide flexible non-parametric models of data and we are using them to model temporal and spatial patterns in gene expression. Single-cell omics measurements are destructive and one cannot follow the high-dimensional dynamics of genes across time in one cell. Similarly, the spatial context of cells is often lost or only known with reduced resolution. Computational methods are widely used to infer pseudo-temporal orderings of cells or to infer spatial locations. We show how Gaussian processes (GPs) can be used to model temporal and spatial relationships between genes and cells in these datasets. As examples I will show how we use Bayesian GPLVMs with informative priors to infer pseudo-temporal orderings for single-cell time course data [1] and branching GPs to identify gene-specific bifurcation points across pseudotime [2]. Gene expression data are often summarized as counts and there may be many zero values in the data due to limited sequencing depth. We therefore recently extended these methods to use negative binomial or zero-inflated negative binomial likelihoods and we show that this can lead to much improved performance over standard Gaussian noise models when identifying spatially varying genes from spatial transcriptomics data [3].

[1] Ahmed, S., Rattray, M., & Boukouvalas, A. (2019). GrandPrix: scaling up the Bayesian GPLVM for single-cell data. Bioinformatics, 35(1), 47-54.

[2] Boukouvalas, A., Hensman, J., & Rattray, M. (2018). BGP: identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process. Genome biology, 19(1), 65.

[3] BinTayyash, N., Georgaka, S., John, S. T., Ahmed, S., Boukouvalas, A., Hensman, J., & Rattray, M. (2020). Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments. Bioarxiv https://doi.org/10.1101/2020.07.29.227207

Short bio: Magnus Rattray is Professor of Computational and Systems Biology at the University of Manchester and Director of the Institute for Data Science & AI. He works on the development of methods for machine learning and Bayesian inference with applications to large-scale biological and medical datasets. He has a long-standing interest in longitudinal data analysis and a more recent interest in modelling single-cell, spatial omics and live cell imaging microscopy data. He is a Fellow of the ELLIS Health Programme and the Alan Turing Institute and his research is funded by a Wellcome Trust Investigator Award.

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Livestream / Virtual Tue, 06 Oct 2020 13:35:21 -0400 2020-10-14T16:00:00-04:00 2020-10-14T17:00:00-04:00 Off Campus Location DCMB Seminar Series Livestream / Virtual Magnus Rattray, PhD (Professor of Computational and Systems Biology, University of Manchester)
LHS Collaboratory-LHS as a Driver of Diversity, Equity, and Inclusion (October 20, 2020 11:30am) https://events.umich.edu/event/77545 77545-19879862@events.umich.edu Event Begins: Tuesday, October 20, 2020 11:30am
Location: Off Campus Location
Organized By: Department of Learning Health Sciences

Healthcare and health remain unconscionably inequitable. This year, the disproportionate toll the COVID-19 pandemic has taken on those historically least well-served by our health system, has highlighted the pressing societal challenge of health disparities.

Beyond simply striving to do no harm, Learning Health Systems (LHSs) have the potential to serve as forces for justice in healthcare and health; indeed, they can be powerful drivers of diversity, equity, and inclusion. LHSs are anchored in multi-stakeholder consensus Core Values that explicitly incorporate principles such as inclusiveness, transparency, and accessibility. Their proximal goal is "to efficiently and equitably serve the learning needs of all participants, as well as the overall public good."

The October 2020 LHS Collaboratory will share lessons from health advocates working on the front lines to make healthcare and health more equitable. These thought leaders and do-ers will illuminate the transformative power of LHSs - and the diverse and inclusive communities of interest that are collaborating to realize them.

Moderator:
Joshua C. Rubin, JD, MBA, MPP, MPH
Program Officer, Learning Health System Initiatives, Department of Learning Health Sciences, University of Michigan

Panelists:
Luis Belén
Chief Executive Officer of the National Health IT Collaborative for the Underserved (NHIT Collaborative)

Danielle Brooks, JD
Director of Health Equity, Amerihealth Caritas

Melissa S. Creary, PhD, MPH, Assistant Professor
Department of Health Management and Policy
School of Public Health, University of Michigan

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Lecture / Discussion Sun, 27 Sep 2020 21:18:37 -0400 2020-10-20T11:30:00-04:00 2020-10-20T13:00:00-04:00 Off Campus Location Department of Learning Health Sciences Lecture / Discussion LHS Collaboratory Logo puzzle pieces
DCMB / CCMB Weekly Seminar (October 21, 2020 4:00pm) https://events.umich.edu/event/78531 78531-20058232@events.umich.edu Event Begins: Wednesday, October 21, 2020 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract
Although machine learning applications are now pervasive to every industry, adoption into healthcare remains a challenging and arduous process. Barriers to implementation include clinician trust, algorithm credibility and actionability, promoting clinician literacy in machine learning methods, and mitigating unintended consequences.

In the high-risk operating room setting, anesthesiologists are recognized leaders in patient safety, and manage uncertainty through careful considerations of risk and benefit based upon a thorough understanding of disease processes and treatment mechanisms. In this talk, the speaker highlights how obstacles to implementation of machine-learning based healthcare applications can be mitigated, and how an understanding of such applications can be promoted among clinically-minded anesthesiologists who may not necessarily be expert data scientists.

Short Bio:
Dr. Mathis has research interests in improving perioperative care for patients with advanced cardiovascular disease, particularly for patients with heart failure. As part of the Multicenter Perioperative Outcomes Group (MPOG), an international consortium of perioperative databases for which U-M serves as the coordinating center, he serves as Associate Research Director and plays a lead role in integration of MPOG data with data from national cardiac and thoracic surgery registries. He also has interests in leveraging novel data science methods to understand patterns within highly granular intraoperative physiologic data, studying hemodynamic responses to surgical and anesthetic stimuli as a means for early detection of cardiovascular diseases such as heart failure.

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Livestream / Virtual Wed, 14 Oct 2020 11:43:15 -0400 2020-10-21T16:00:00-04:00 2020-10-21T17:00:00-04:00 Off Campus Location DCMB Seminar Series Livestream / Virtual Image which promotes the content of Dr. Mathis' talk (https://jamanetwork.com/collections/5584/critical-care-medicine)
Special Joint Seminar - Hosted by DCMB, Department of Mathematics, and the Smale Institute (October 26, 2020 12:00pm) https://events.umich.edu/event/78673 78673-20099541@events.umich.edu Event Begins: Monday, October 26, 2020 12:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Dr. Leland Hartwell won the Nobel Prize in Physiology or Medicine in 2001 for the discoveries of key regulators of the cell cycle.

“We want our students to have an authentic experience of science. Nearly all science activities designed for schools require the students to demonstrate an established scientific principle by getting the right answer. Getting the “right” answer is not authentic science. Science is the exploration of the unknown – the answer cannot be known.“
- Leland Hartwell

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Livestream / Virtual Mon, 19 Oct 2020 13:04:27 -0400 2020-10-26T12:00:00-04:00 2020-10-26T13:00:00-04:00 Off Campus Location DCMB Seminar Series Livestream / Virtual Dr. Leland Hartwell, Nobel Laureate
DCMB / CCMB Seminar (October 28, 2020 4:00pm) https://events.umich.edu/event/78528 78528-20058229@events.umich.edu Event Begins: Wednesday, October 28, 2020 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract: Single-cell RNA sequencing (scRNA-seq) allows researchers to examine the transcriptome at the single-cell resolution and has been increasingly employed as technologies continue to advance. Due to technical and biological reasons unique to scRNA-seq data, clustering and batch effect correction are almost indispensable to ensure valid and powerful data analysis. Multiple methods have been proposed for these two important tasks. For clustering, we have found that different methods, including state-of-the-art methods such as Seurat, SC3, CIDR, SIMLR, t-SNE + k-means, yield varying results in terms of both the number of clusters and actual cluster assignments. We have developed ensemble methods, SAFE-clustering and SAME-clustering, that leverages hyper-graph partitioning algorithms and a mixture model-based approach respectively to produce more robust and accurate ensemble solution on top of clustering results from individual methods. For batch effect correction, we have developed methods based on supervised mutual nearest neighbor detection to harness the power of known cell type labels for certain single cells. We benchmarked all methods in various scRNA-seq datasets to demonstrate their utilities.

Short bio: Yun Li, PhD is an Associate professor of Genetics and Biostatistics at the University of North Carolina, Chapel Hill. Dr. Li is a statistical geneticist with extensive experiences with method development and application on genotype imputation (developer of MaCH and MaCH-admix), genetic studies of recently admixed population, design and analysis of sequencing-based studies, analyses of multi-omics data including mRNA expression, DNA methylation and chromatin three dimensional organization. Dr. Li has been playing an active role in genetic studies of complex human traits resulting many GWAS and meta-analysis publications, including >30 in Nature, Science, Cell, and Nature Genetics. Dr. Li has been leading multiple R01 projects on statistical method development for complex trait genetics. Dr. Li has also been the Director for the Data Science Core of IDDRC (Intellectual and Developmental Disabilities Research Center). Dr. Li has received many awards and became the Thomson Reuters Highly Cited Researcher due to her high impact scientific work. Specifically, her work has been cited >60,000 times with h-index of 64 and i10-index of 113.

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Livestream / Virtual Wed, 14 Oct 2020 10:41:20 -0400 2020-10-28T16:00:00-04:00 2020-10-28T17:00:00-04:00 Off Campus Location DCMB Seminar Series Livestream / Virtual Yun Li, PhD (Associate Professor of Genetics & Biostatistics; Adjunct Associate Professor, Applied Physical Sciences at School of Medicine, Genetics at University of North Carolina)
DCMB / CCMB Weekly Seminar (November 4, 2020 4:00pm) https://events.umich.edu/event/78770 78770-20121164@events.umich.edu Event Begins: Wednesday, November 4, 2020 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract: Metabolomics is a powerful approach to characterize small molecules produced in cells, tissues, and other biological systems. Metabolites are direct products of enzymatic reactions and provide a snapshot of cellular activities. Metabolomics-based research has already had a profound impact on biomarker discovery, nutritional analysis, and other biomedical and biological discoveries. The most pressing problem in metabolomics however is identifying compounds in the sample-under-study from the metabolomics measurements. Current analysis tools are capable of annotating only a small portion of sample measurements.

In this talk, we present machine learning solutions to three challenges related to the interpretation of metabolomics data. To mimic the function of a mass spectrometer in generating a mass spectrum, we use graph neural networks to translate a molecular structure into its respective spectral signature. To interpret the biological measurements in the context of the biological sample, we use Bayesan learning to deduce the likelihood of pathway activities. To suggest putative candidate molecules that are biologically relevant matches to the measured spectra, we explore several methods for predicting possible enzymatic products. We discuss several results, highlighting the value of using machine learning for advancing metabolomics analysis.

Short bio: Soha Hassoun is Professor and Past Chair of the Department of Computer Science at Tufts University. Soha received her undergraduate degree in Electrical Engineering from South Dakota State University, the Master's degree from MIT, and the Ph.D. degree from the Department of Computer Science and Engineering, University of Washington in Seattle. Soha’s lab uses Machine Learning to develop analysis and discovery tools for synthetic and systems biology, with a focus on enzyme promiscuity prediction and metabolomics analysis. Soha was a recipient of the NSF CAREER Award, and several technical and service awards from various professional societies. She provided technical leadership for several conferences including ICCAD and DAC. She co-founded the International Workshop on Bio-Design Automation in 2009. Soha serves on the board of the Computing Research Association's Committee on Widening Participation in Computing Research.

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Livestream / Virtual Thu, 22 Oct 2020 11:33:23 -0400 2020-11-04T16:00:00-05:00 2020-11-04T17:00:00-05:00 Off Campus Location DCMB Seminar Series Livestream / Virtual
DCMB / CCMB Weekly Seminar (November 11, 2020 4:00pm) https://events.umich.edu/event/79286 79286-20264787@events.umich.edu Event Begins: Wednesday, November 11, 2020 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract: There is a growing understanding that stress and depression during the process of training to become physicians is high. In this talk, we will discuss how we have used mobile and wearable data as well as genomics to understand the prevalence in the US and China, drivers and possible solutions about training physician depression and how the COVID-19 pandemic has affected them in the two countries.

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Livestream / Virtual Mon, 09 Nov 2020 14:13:58 -0500 2020-11-11T16:00:00-05:00 2020-11-11T17:00:00-05:00 Off Campus Location DCMB Seminar Series Livestream / Virtual Drs. Margit Burmeister and Srijan Sen
Reflections on Learning to Improve: Foundational Ideas, Observations from Practice, and Building a Field (November 12, 2020 12:00pm) https://events.umich.edu/event/78908 78908-20152763@events.umich.edu Event Begins: Thursday, November 12, 2020 12:00pm
Location: Off Campus Location
Organized By: Department of Learning Health Sciences

While the LHS Collaboratory is typically focused on learning health, learning systems actually have very broad applicability. Moreover, there has been a strong interest in the Collaboratory from the education community which is also focused on learning systems.

A thought leader in this area, Anthony S. Bryk, President of the Carnegie Foundation for the Advancement of Teaching, will be speaking about a set of critical observations acquired in the course of his own efforts to improve how large complex educational systems work.

Discussants:

Elizabeth Birr Moje, Dean,
George Herbert Mead Collegiate Professor of Education,
and Arthur F. Thurnau Professor School of Education
Faculty Associate in the Institute for Social Research; Latino/a
Studies; and the Joint Program in English & Education
University of Michigan

Caren M. Stalburg, MD, MA
Collaborative Lead for Education
Associate Professor of Learning Health Sciences
Associate Professor of Obstetrics and Gynecology
Director of HILS Online Masters
University of Michigan

Moderator:

Donald J. Peurach, PhD
Professor
University of Michigan School of Education
Senior Fellow, Carnegie Foundation for the Advancement of
Teaching

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Lecture / Discussion Mon, 26 Oct 2020 12:41:04 -0400 2020-11-12T12:00:00-05:00 2020-11-12T13:30:00-05:00 Off Campus Location Department of Learning Health Sciences Lecture / Discussion Collaboratory logo
DCMB / CCMB Weekly Seminar (November 18, 2020 4:00pm) https://events.umich.edu/event/79290 79290-20264791@events.umich.edu Event Begins: Wednesday, November 18, 2020 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract: Genetic variation affecting gene expression is wide-spread within and among species. This variation reflects the combined actions of mutation introducing new genetic variants and selection eliminating deleterious ones. Comparative studies of gene expression in fruit flies, yeast, plants, and mice have shown that the relative contributions of cis- and trans-acting variants to expression differences change over evolutionary time, indicating that selection has different effects on cis- and trans-regulatory variants. To better understand the reasons for this now widely observed pattern, we have been systematically studying the effects of mutation and selection on expression of the TDH3 gene of the baker’s yeast Saccharomyces cerevisiae. This work has revealed differences between cis- and trans-regulatory mutations in their frequency, effects, and dominance. Differences in pleiotropy are also generally assumed to exist between cis- and trans-regulatory that affect their evolutionary fate, but have been difficult to measure. In this talk, I will discuss how newly arising cis- and trans-regulatory mutations affecting expression of this focal gene are structured within the regulatory network, their pleiotropic effects on expression of all other genes in the genome, and how these pleiotropic effects influence fitness. A computational model of regulatory evolution integrating empirically observed differences in properties of cis- and trans-regulatory mutations will also be presented and discussed.

Patricia Wittkopp received a BS from the University of Michigan, a PhD from the University of Wisconsin, and did postdoctoral work at Cornell University. In 2005, she began a faculty position at the University of Michigan, where she is now the Sally L. Allen Collegiate Professor and Arthur F Thurnau Professor in the Department of Ecology and Evolutionary Biology and the Department of Molecular, Cellular, and Developmental Biology, and is a member of the Center for Computational Medicine and Bioinformatics. Her research investigates the genetic basis of phenotypic evolution, with an emphasis on the evolution of gene expression. She was a Damon Runyon Cancer Research Fellow, an Alfred P Sloan Research Fellow, Guggenheim Fellow, and a recipient of a March of Dimes Starter Scholar Award, the Margaret Dayhoff Mid-Career Award from the Society of Molecular Biology and Evolution, and the Friedrich Wilhelm Bessel Research Award from the Alexander von Humboldt Foundation.

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Livestream / Virtual Mon, 09 Nov 2020 15:12:34 -0500 2020-11-18T16:00:00-05:00 2020-11-18T17:00:00-05:00 Off Campus Location DCMB Seminar Series Livestream / Virtual
DCMB / CCMB Weekly Seminar (December 2, 2020 4:00pm) https://events.umich.edu/event/79631 79631-20436379@events.umich.edu Event Begins: Wednesday, December 2, 2020 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

ABSTRACT: The brain is made of networks of neurons that send information to each other via spikes. Sleep and wake are the most clearly definable brain states and each exerts unique effects upon neural network spiking activity. We used large-scale recordings in the frontal cortex of mice and rats to examine the activity of neurons during wake/sleep cycles and found that a novel form of homeostatic action is taken by sleep: homogenization of firing rates. Whereas it was previously believed that sleep simple decreased firing rates, we found that this was much more true of the most active neurons only, thereby reducing the variance of the population.

To extend this observation of homeostatic forced during sleep we also examine how sleep and wake states interact with learning and performance, which is also facilitated by sleep. We have therefore begun to record before, during and after learning sessions to determine how learning interacts with the usual homeostatic effects of sleep. Further we can also record how waking changes in brain states such as motivation and attention modulate firing and information processing by neurons during behavior itself.

Finally, our end-goal to translate these kinds of basic neurobiologic observations in healthy rodents to states of stress or treatments of stress. Unfortunately the chronic stress states of relevance to psychiatric disease do not last seconds but days and weeks. We have therefore begun to build new long-term recording environments to enable future experiments over these time-spans.

BIOGRAPHY:
Dr. Watson is an assistant professor in psychiatry at the University of Michigan. He grew up in Ann Arbor and then obtained his BA from Cornell University and his M.D. and Ph.D. degrees from Columbia University. During his Ph.D. he used two-photon microscopy to study the behavior of neurons in local cortical microcircuits. During his doctoral work he also participated in technical development of multi-beam two photon imaging techniques. Upon graduation from medical school, Dr. Watson pursued a residency in Psychiatry at Weill Cornell Medical College as well postdoctoral work at New York University. He received the National Institute for Mental Health’s Outstanding Resident Award, the American Psychiatric Association’s Lilly Research Fellowship and the Leon Levy Neuroscience Fellowship. He did a fellowship with Dr. Gyorgy Buzsaki at NYU to record ongoing activity in naturally behaving and sleeping animals wherein he showed that sleep reorganizes neuronal firing architecture in the neocortex in previously unknown ways. He is now combining his electrical recordings with behavioral tools to deepen his understanding of both use and regulation of cortical brain circuits.

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Livestream / Virtual Tue, 01 Dec 2020 09:45:44 -0500 2020-12-02T16:00:00-05:00 2020-12-02T17:00:00-05:00 Off Campus Location DCMB Seminar Series Livestream / Virtual
Department of Computational Medicine & Bioinformatics Weekly Wednesday Seminar (December 9, 2020 4:00pm) https://events.umich.edu/event/79756 79756-20484062@events.umich.edu Event Begins: Wednesday, December 9, 2020 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Learning objectives:

1. Discuss the conceptual distinction and clinical utility of self-reported race/ethnicity and genetic ancestry in childhood asthma.
2. Discuss the role of genetic ancestry and socio-environmental exposures in childhood asthma.
3. Discuss ancestry-specific polygenic risk scores, precision medicine and childhood asthma disparities.

Short bio: Dr. Mersha is currently an Associate Professor in the Division of Asthma Research and leads the Population Genetics, Ancestry, and Bioinformatics (pGAB) Laboratory (https://research.cchmc.org/mershalab/Home.php).
Dr. Mersha’s research combines quantitative, ancestry and statistical genomics to unravel genetic and non-genetic contributions to complex diseases and racial disparities in human populations, particularly asthma and asthma-related allergic disorders. Much of his research is at the interface of genetic ancestry, statistics, bioinformatics, and functional genomics, and he is interested in cross-line disciplines to unravel the interplay between genome and envirome underlying asthma risk. His long-term research goal is to understand and dissect how biologic predisposition and environmental exposures interact to shape racial disparities in complex disorders.

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Livestream / Virtual Mon, 07 Dec 2020 11:27:42 -0500 2020-12-09T16:00:00-05:00 2020-12-09T17:15:00-05:00 Off Campus Location DCMB Seminar Series Livestream / Virtual Tesfaye ("Tes") Mersha, PhD (Associate Professor, Division of Asthma Research at Cincinnati Children's Hospital Medical Center)
LHS Collaboratory (January 21, 2021 11:30am) https://events.umich.edu/event/80293 80293-20688136@events.umich.edu Event Begins: Thursday, January 21, 2021 11:30am
Location: Off Campus Location
Organized By: Department of Learning Health Sciences

The LHS Collaboratory presents Rachel Richesson, PhD, MPH, MS, FACMI, Professor of Learning Health Sciences, Department of Learning Health Sciences at the University of Michigan in a virtual event on 1/21/2021 from 11:30 am to 1:00 pm ET.

Professor Richesson's talk, "Data Standards and Learning Health Systems –Challenges and Opportunities," will be followed by an audience Q&A. Questions are also encouraged prior to the event.

Please send questions to LHSCollaboratory-info@umich.edu.

Registration in advance at: https://umich-health.zoom.us/webinar/register/WN_HytRsYwITc6oOGRj0F_MOA

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Livestream / Virtual Sat, 02 Jan 2021 10:24:08 -0500 2021-01-21T11:30:00-05:00 2021-01-21T13:00:00-05:00 Off Campus Location Department of Learning Health Sciences Livestream / Virtual LHS Collaboratory logo
Department of Computational Medicine & Bioinformatics Seminar (January 27, 2021 4:00pm) https://events.umich.edu/event/80722 80722-20777538@events.umich.edu Event Begins: Wednesday, January 27, 2021 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract: Massively parallel single-cell and single-nucleus RNA sequencing (sc/snRNA-seq) has opened the way to systematic tissue atlases in health and disease, but as the scale of data generation is growing, so is the need for computational pipelines for scaled analysis. We developed Cumulus, the first comprehensive cloud-based framework, to address the big data challenge arising from sc/snRNA-seq analysis. Cumulus combines the power of cloud computing with improvements in algorithm and implementation to achieve high scalability, low cost, user-friendliness and integrated support for a comprehensive set of features. We benchmark Cumulus on the Human Cell Atlas Census of Immune Cells dataset of bone marrow cells and show that it substantially improves efficiency over conventional frameworks, while maintaining or improving the quality of results, enabling large-scale studies.

In recent years, biologists have found that sc/snRNA-seq alone is not enough to reveal the full picture of how cells function and coordinate with each other in a complex tissue. They begin to couple sc/snRNA-seq with other common data modalities, such as single-cell ATAC-seq (scATAC-seq), single-cell Immune Repertoire sequencing (scIR-seq), spatial transcriptomics and mass cytometry. This data coupling is called single-cell multimodal omics. As it is becoming a new common practice, new analysis needs emerge along with two major computational challenges: big data challenge and integration challenge. The big data challenge requires us to develop scalable computational infrastructure and algorithms to deal with the ever-growing large datasets produced from the community. The integration challenge requires us to design new algorithms to enable holistic integration of heterogeneous data from different modalities. In the last part of my talk, I will discuss my team’s efforts and plans to develop Cumulus as an integrated data analysis framework for scaled single-cell multimodal omics.

Single-cell multimodal omics has the potential to provide a more comprehensive characterization of complex multicellular systems than the sum of its parts. As the datasets produced from the community keep growing substantially, the enhanced Cumulus will continue playing an important role in the effort to build atlases of complex tissues and organs at higher cellular resolution, and in leveraging them to understand the human body in health and disease.

Short bio: Dr. Bo Li is an assistant professor of medicine at Harvard Medical School, the director of Bioinformatics and Computational Biology at Center for Immunology Inflammatory Diseases, Massachusetts General Hospital, and an associate member of the Broad Institute of MIT and Harvard. His research focuses on large-scale single-cell and single-nucleus genomics data analysis. He received his Ph.D. in computer science from UW-Madison and completed two postdoctoral trainings with Dr. Lior Pachter at UC Berkeley and Dr. Aviv Regev at Broad Institute. He is best known for developing RSEM, an impactful RNA-seq transcript quantification software. RSEM is cited 9,384 times (Google Scholar) and adopted by several big consortia such as TCGA, ENCODE, GTEx and TOPMed.

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Livestream / Virtual Wed, 13 Jan 2021 14:32:34 -0500 2021-01-27T16:00:00-05:00 2021-01-27T17:00:00-05:00 Off Campus Location DCMB Seminar Series Livestream / Virtual Bo Li, PhD (Assistant Professor at Harvard Medical School in Boston, MA)
CCMB / DCMB Weekly Seminar Series (February 3, 2021 4:00pm) https://events.umich.edu/event/81571 81571-20927558@events.umich.edu Event Begins: Wednesday, February 3, 2021 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract:
Understanding intra-tumour heterogeneity (ITH), in particular identifying the presence of subclonal populations of cancer cells that may respond differently to treatments, is key to support precision medicine approaches. Capturing ITH from genomic measures raises however a number of computational challenges. In this talk I will present CloneSig, a method to infer ITH from "bulk" genomic data, in particular whole-exome sequencing data, and capture changes in mutational processes active in different subclones. I will then discuss the promises of single-cell genomics and some challenges it raises, in particular to transform raw count data into useful representations, integrate heterogeneous modalities, and learn gene regulation.

Short bio: Jean-Philippe Vert has been a research scientist at Google Brain in Paris and adjunct researcher at PSL University Mines ParisTech since 2018. He graduated from Ecole Polytechnique and holds a PhD in mathematics from Paris University. He was research professor and the founding director of the Centre for Computational Biology at Mines ParisTech from 2006 to 2018, team leader at the Curie Institute on computational biology of cancer (2008-2018), visiting scholar at UC Berkeley (2015-2016), and professor at the department of mathematics of Ecole normale supérieure in Paris (2016-2018).
His research interest concerns the development of statistical and machine learning methods, particularly to model complex, high-dimensional and structured data, with an application focus on computational biology, genomics and precision medicine. His recent contributions include new methods to embed structured data such as strings, graphs or permutations to vector spaces, regularization techniques to learn from limited amounts of data, and computationally efficient techniques for pattern detection and feature selection.
He is also working on several medical applications in cancer research, including quantifying and modeling cancer heterogeneity, predicting response to therapy, and modeling the genome and epigenome of cancer cells at the single-cell level.

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Livestream / Virtual Mon, 01 Feb 2021 14:12:04 -0500 2021-02-03T16:00:00-05:00 2021-02-03T17:00:00-05:00 Off Campus Location DCMB Seminar Series Livestream / Virtual Jean-Philippe Vert, PhD (Research Scientist at Google Brain in Paris, Adjunct Researcher at PSL University Mines ParisTech)
CCMB / DCMB Weekly Seminar (February 10, 2021 4:00pm) https://events.umich.edu/event/81413 81413-20893777@events.umich.edu Event Begins: Wednesday, February 10, 2021 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract: The increasing omics data and advanced AI technology present a great opportunity for novel biomarker-driven cancer therapies. My talk will cover two parts. First, I will introduce DrBioRight, a natural language-oriented and AI-driven analytic platform for omic data analysis. This platform allows users to perform analysis directly through human languages and it improves the performance through adaptive learning. Armed with NLP and AI technologies, this analytic will maximize the utility of omics data and lead to a new paradigm for biomedical research. Second, I will discuss our recent work on enhancer RNAs. We show that the eRNAs provide explanatory power for cancer phenotypes beyond that provided by mRNA expression through resolving intratumoral heterogeneity with enhancer cell-type specificity. Our study provides a high-resolution map of eRNA loci through which enhancer activities can be quantified by RNA-seq, enabling a broad range of biomedical investigations.

Bio: Dr. Liang is a Barnhart Family Distinguished Professor in Targeted Therapies and the Deputy Chair of Department of Bioinformatics and Computational Biology at the University of Texas MD Anderson Cancer Center. He is also a professor in the Department of Systems Biology. He received his B.S. in chemistry from Peking University (China) in 2001 and Ph.D. in quantitative and computational biology from Princeton University (NJ, USA) in 2006. Dr. Liang then finished his postdoctoral training in evolutionary and computational genomics at the University of Chicago. He joined MD Anderson Cancer Center as Assistant Professor and started his own group in 2009.
At MD Anderson, Dr. Liang’s group focuses on bioinformatics tool development, integrated cancer genomic analysis, regulatory RNA regulation/modification, and cancer systems biology. His systematic studies on enhancer regulation, RNA editing, functional proteomics, sex effects, and driver mutations in cancer have generated profound impacts on the biomedical research community and attracted wide attention such as The Wall Street Journal and Newsweek. The bioinformatics tools his group developed (such as TCPA, TANRIC, FASMIC, DrBioRight) collectively have >110,000 active users worldwide. Since 2010, he has published >140 papers total citation >25,000 times), including 41 corresponding-author papers in top journals such as Cell, Cancer Cell, Nature Genetics, Nature Biotechnology, and Nature Methods.
Dr. Liang has taken leadership roles in large cancer consortium projects, including chair of The Cancer Genome Atlas (TCGA) PanCanAtlas working groups, one co-leader of International Cancer Genome Consortium (ICGC) Pan-Cancer Whole Genome Analysis Project, and one co-chair of NCI Genomic Data Commons (GDC) QC working group. He won several awards including MD Anderson R. Lee Clark Fellow Award (2014), the University of Texas System STARS Award (2015), MD Anderson Faculty Scholar Award (2018), and AACR Team Science Award (2020). He is an elected Fellow of American Association for the Advancement of Science (AAAS).

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Livestream / Virtual Thu, 28 Jan 2021 11:33:05 -0500 2021-02-10T16:00:00-05:00 2021-02-10T17:00:00-05:00 Off Campus Location DCMB Seminar Series Livestream / Virtual Han Liang, PhD Professor and Deputy Chair, Department of Bioinformatics and Computational Biology Professor, Department of Systems Biology Barnhart Family Distinguished Professor in Targeted Therapies The University of Texas MD Anderson Cancer Center
Special Joint Seminar between our Department and the Genome Science Training Program (February 17, 2021 4:00pm) https://events.umich.edu/event/80415 80415-20719669@events.umich.edu Event Begins: Wednesday, February 17, 2021 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract: The human genome sequence folds in three dimensions (3D) into a rich variety of locus-specific contact patterns. Despite growing appreciation for the importance of 3D genome folding in evolution and disease, we lack models for relating mutations in genome sequences to changes in genome structure and function. Towards that goal, we discovered that the organization of gene regulatory domains within chromosomes and the specific sequences that sit at boundaries between domains are under strong negative selection in the human population and over primate evolution. Motivated by this signature of functional importance, we developed a deep convolutional neural network, called Akita, that accurately predicts genome folding from DNA sequence alone. Representations learned by Akita underscore the importance of the structural protein CTCF but also reveal a complex grammar beyond CTCF binding sites that underlies genome folding. Akita enabled rapid in silico predictions for effects of sequence mutagenesis on the 3D genome, including differences in genome folding across species and in disease cohorts, which we are validating with CRISPR-edited genomes. This prediction-first strategy exemplifies my vision for a more proactive, rather than reactive, role for data science in biomedical research.

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

Short bio: Dr. Katherine S. Pollard is Director of the Gladstone Institute of Data Science & Biotechnology, Investigator at the Chan Zuckerberg Biohub, Professor in the Department of Epidemiology & Biostatistics and Bioinformatics Graduate Program at UCSF. Her lab develops statistical models and open source bioinformatics software for the analysis of massive genomic datasets. Previously, Dr. Pollard was an assistant professor in the University of California, Davis Genome Center and Department of Statistics. She earned her PhD in Biostatistics from the University of California, Berkeley and was a comparative genomics postdoctoral fellow at the University of California, Santa Cruz. She was awarded the Thomas J. Watson Fellowship, the Sloan Research Fellowship, and the Alumna of the Year from UC Berkeley. She is a Fellow of the International Society for Computational Biology and of the California Academy of Sciences.

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Livestream / Virtual Wed, 06 Jan 2021 09:24:05 -0500 2021-02-17T16:00:00-05:00 2021-02-17T17:00:00-05:00 Off Campus Location DCMB Seminar Series Livestream / Virtual Katherine S. Pollard, PhD (Director, Gladstone Institute of Data Science & Biotechnology; Professor, UCSF; Investigator, Chan Zuckerberg Biohub)
LHS Collaboratory- February session (February 23, 2021 12:00pm) https://events.umich.edu/event/81035 81035-20838675@events.umich.edu Event Begins: Tuesday, February 23, 2021 12:00pm
Location: Off Campus Location
Organized By: Department of Learning Health Sciences

The keynote presentation (12:00 pm-1:15 pm ET) will be followed by breakout sessions (1:15 pm-2:15 pm ET) on topics presented by the UM faculty and guests.
Zoom links to the individual breakout sessions are listed below.

Keynote speaker: Dr. Bernardo Mariano, Jr.
Topic: Digital Transformation in Healthcare for a Diverse World
Director of Digital Health & Innovation
Chief Information Officer (CIO)
World Health Organization (WHO)

Remarks:
Laurie McCauley, DDS, MS, PhD
Dean, William K and Mary Anne Najjar Professor of Periodontics
University of Michigan School of Dentistry


Breakout sessions from 1:15 pm-2:15 pm (ET)

Breakout Session #1 LHS and Pain
Zoom link: https://umich.zoom.us/j/99190944947

Topic: Integrating Diverse Health Ecosystems for
Optimal Pain Treatment, Education and Research
Alex F. DaSilva, DDS, DMedSc
University of Michigan School of Dentistry

Perspective: Data De‐Identification and Clinical Decision Support
Ivo Dinov, Ph.D.
Department of Health Behavior and Biological Sciences
University of Michigan


Breakout Session #2 LHS and Caries Risk
Zoom link: https://umich.zoom.us/j/97070468943

Topic: Caries Risk Prediction Models
Margherita Fontana, DDS, PhD
University of Michigan School of Dentistry

Perspective: LHS and Evidence-based Clinical Practice
Alonso Carrasco-Labra, DDS, MSc, PhD
Department of Evidence Synthesis and Translation Research
American Dental Association

Breakout Session #3 LHS and Opioids
Zoom link: https://umich.zoom.us/j/96029888703


Topic: Iteratively Learning about Dental Opioid Prescribing
Romesh Nalliah, BDS, MHCM
University of Michigan School of Dentistry

Perspective: Precision Health in Opioid Management
Chad Brummett, M.D.
University of Michigan Department of Anesthesiology

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Lecture / Discussion Thu, 21 Jan 2021 09:43:32 -0500 2021-02-23T12:00:00-05:00 2021-02-23T14:15:00-05:00 Off Campus Location Department of Learning Health Sciences Lecture / Discussion LHS Collaboratory Logo
CCMB / DCMB Weekly Seminar (February 24, 2021 4:00pm) https://events.umich.edu/event/82197 82197-21052530@events.umich.edu Event Begins: Wednesday, February 24, 2021 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract: COVID Moonshot is an international consortium aiming to discover patent-free oral antiviral against SARS-CoV-2, targeting the main protease. Operating under an open science ethos, we make all data and structures publicly available, and crowdsource molecule designs from the community. In less than a year, we went from fragment hits to nanomolar leads in biochemical and antiviral assays. In my talk, I will discuss Moonshot’s journey towards orally bioavailable, non-covalent, and non-peptidomimetic Mpro inhibitors. I will discuss how machine learning technologies have accelerated our design-make-test cycle, and the learnings we gleaned from this large-scale prospective use of algorithms.

Bio: Dr. Alpha Lee is a Group Leader in the Department of Physics, University of Cambridge. His research focuses on developing machine learning technologies that close the design-make-test cycle for small molecule drug discovery and materials discovery. He is interested in how physical and chemical insights can be integrated into the design of interpretable algorithms. Before joining Cambridge, Dr. Lee was a Fulbright Scholar at Harvard and obtained his PhD from the University of Oxford.

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Livestream / Virtual Wed, 17 Feb 2021 13:18:31 -0500 2021-02-24T16:00:00-05:00 2021-02-24T17:00:00-05:00 Off Campus Location DCMB Seminar Series Livestream / Virtual
CCMB / DCMB Weekly Seminar Series (March 10, 2021 4:00pm) https://events.umich.edu/event/82479 82479-21108092@events.umich.edu Event Begins: Wednesday, March 10, 2021 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract: Single-cell technologies have transformed biomedical research in the last few years. With single-cell sequencing, we can now simultaneously measure thousands of genomics features in a large number of cells, which provides an ultrahigh resolution phenotypic map for each individual. However, single-cell protocols are complex. Even with the most sensitive platforms, the data are often sparse and noisy. Recent development of single-cell multi-omics and spatial transcriptomics technologies further imposed additional challenges on data integration. In this talk, I will present several machine learning methods that my group recently developed for single-cell and spatial transcriptomics data analysis. I will discuss methods for simultaneous denoising, clustering and batch effect correction, single-cell multi-omics data integration, identification of spatially variable genes, generation of super-resolution gene expression, and inference of cell type distribution in spatial transcriptomics. I will illustrate our methods by showing results from ongoing collaborations on cardiometabolic disease and applications to brain and cancer data.
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Biography: Dr. Li’s research interests include statistical genetics and genomics, bioinformatics, and computational biology. The central theme of her current research is to use statistical and computational approaches to understand cellular heterogeneity in human-disease-relevant tissues, to characterize gene expression diversity across cell types, to study the patterns of cell state transition and crosstalk of various cells using data generated from single-cell and spatial transcriptomics studies, and to translate these findings to the clinics. In addition to methods development, Dr. Li is also interested in collaborating with researchers seeking to identify complex disease susceptibility genes and acting cell types. She is Director of Biostatistics for the Gene Therapy Program at Penn, where she advises biostatistics and bioinformatics analysis for various gene therapy studies. She is also Chair of the Graduate Program in Biostatistics. Dr. Li actively serves in the scientific community. She served as a regular member for the NIH Genomics, Computational Biology and Technology (GCAT) study section for 6 years, and the NHGRI Center for Inherited Disease Research (CIDR) for 3 years. She is an Associate Editor of Annals of Applied Statistics, Statistics in Biosciences, PLOS Computational Biology, and Human Genetics and Genomics Advances. She is an elected member of the International Statistical Institute and a Fellow of the American Statistical Association.

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Livestream / Virtual Wed, 24 Feb 2021 12:57:46 -0500 2021-03-10T16:00:00-05:00 2021-03-10T17:00:00-05:00 Off Campus Location DCMB Seminar Series Livestream / Virtual
CCMB / DCMB Weekly Seminar Series featuring Sriram Chandrasekaran (Assistant Professor, Biomedical Engineering) (March 17, 2021 4:00pm) https://events.umich.edu/event/82825 82825-21179592@events.umich.edu Event Begins: Wednesday, March 17, 2021 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract: Drug combinations have the potential to greatly expand our pharmacopeia while reducing both cost and drug resistance. Yet the current drug-discovery approach is unable to screen the astronomical number of possible combinations in different cell types and does not account for the complex environment inside the body. We have developed AI tools - INDIGO and MAGENTA - that predict the efficacy of drug combinations based on the properties of the drugs, the pathogen, and the infection environment. We are also using modeling to identify drugs that work in synergy with the host immune system. Using INDIGO and MAGENTA, we have identified highly synergistic combinations of repurposed drugs to treat drug resistant infections including Tuberculosis, the deadliest bacterial infection. INDIGO also accurately predicts the outcome of past clinical trials of drug combinations. Our ultimate goal is to create a personalized approach to treat infections using AI.
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Biography: Chandrasekaran received his bachelor’s degree in Biotechnology from Anna University in 2008, and a PhD in Biophysics from the University of Illinois at Urbana-Champaign in 2013. He worked at Harvard University and MIT as a Harvard Junior Fellow between 2013 and 2016 and became an Assistant Professor at UM in 2017. His lab develops systems biology algorithms for drug discovery. Computer models from his lab like INDIGO and MAGENTA are being used to design effective therapies against drug resistant pathogens. His lab also develops systems biology algorithms to understand metabolic regulation. The approaches that they have created (PROM, ASTRIX, DFA, EGEM and GEMINI) perform complementary functions in modeling of metabolic and regulatory networks. Chandrasekaran’s research has been published in Cell, Genome Biology, mBio, and PNAS. For his work, Chandrasekaran previously received the 2013 Harvard Junior Fellowship, the 2011 Howard Hughes Medical Institute (HHMI) International Predoctoral Fellowship, the 2014 William Milton Fund award, 2018 UM Precision Health Investigator Award, and the 2018 Distinguished Young Investigator Award from the AICHE COBRA society.


https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Livestream / Virtual Fri, 05 Mar 2021 14:44:14 -0500 2021-03-17T16:00:00-04:00 2021-03-17T17:00:00-04:00 Off Campus Location DCMB Seminar Series Livestream / Virtual Sriram Chandrasekaran, PhD (Assistant Professor, Biomedical Engineering)
CCMB / DCMB Weekly Seminar Series Featuring Duncan K. Ralph (Fred Hutchinson Cancer Research Center) (March 24, 2021 4:00pm) https://events.umich.edu/event/82733 82733-21169592@events.umich.edu Event Begins: Wednesday, March 24, 2021 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract: Antibodies are an integral part of the adaptive immune response, and are a critical component of both vaccine-induced and naturally-acquired immunity. The development of deep sequencing approaches in recent years has allowed us to sample a significant fraction of the diverse repertoire of B cell receptor sequences from which antibodies are made. These sequences encode a wealth of information on the somatic rearrangement and evolutionary processes that determine the contours of our antibody repertoires, and thus our ability to respond appropriately to pathogens and vaccines. Extracting this information, however, requires a careful inference approach across several different analysis steps. I will describe the computational approaches that we have taken to solving these problems, which constitute the partis software package, and describe their application in several projects, including HIV and Dengue data.

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Biography: Duncan attended the University of California at Santa Cruz for his undergraduate studies in physics, completing his thesis on energy transport in condensed matter theory in 2005. He completed his PhD at the Massachusetts Institute of Technology in 2014, working on the Large Hadron Collider at the European particle physics laboratory (CERN). His thesis described the observation of Higgs boson decays to four leptons. Since 2014, he has worked in Frederick Matsen’s lab at the Fred Hutchinson Cancer Research Center, first as a postdoctoral researcher and more recently as a staff scientist, writing new computational methods for the analysis of B cell receptor deep sequencing data.

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Livestream / Virtual Thu, 04 Mar 2021 11:20:24 -0500 2021-03-24T16:00:00-04:00 2021-03-24T17:00:00-04:00 Off Campus Location DCMB Seminar Series Livestream / Virtual
LHS Collaboratory March Session (March 25, 2021 12:00pm) https://events.umich.edu/event/82008 82008-21006745@events.umich.edu Event Begins: Thursday, March 25, 2021 12:00pm
Location: Off Campus Location
Organized By: Department of Learning Health Sciences

Speakers Stefan Boes, PhD and Sarah Mantwill, PhD from the university of Lucerne will discuss the Swiss Learning Health System.

Promoting and supporting uptake of evidence and evidence-informed decision-making in health-systems related policy and practice is a challenge. In Switzerland, the need to address this matter has been increasingly emphasized by different actors in the health system. In particular, the lack of comprehensive coordination efforts in the field of health services research, and subsequent knowledge translation activities, has been stressed. In response, the Swiss Learning Health System (SLHS) was established as a nationwide project in 2017, currently involving 10 academic partner institutions. One of the overarching objectives of the SLHS is to bridge research, policy, and practice by providing an infrastructure that supports learning cycles by: continuously identifying issues relevant to the Swiss health system, systemizing relevant evidence, presenting potential courses of action, and revising and reshaping responses. Key features of learning cycles in the SLHS include the development of policy/evidence briefs that serve as a basis for stakeholder dialogues with actors from research, policy and practice. Issues that are identified to be further pursued are monitored for potential implementation and eventually evaluated to inform new learning cycles and to support continuous learning within the system.

Dr. Boes and Dr. Mantwill will provide an overview of the SLHS and its key features, as well as its capacity building efforts to train young researchers in the field of learning health systems, and the development of a centralized metadata repository in support of creating a sufficient large evidence basis to support learning cycles in the Swiss health system. Further, they will discuss lessons learned from the past and the newest developments of the SLHS in light of a second funding phase supported by the Swiss government.

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Livestream / Virtual Thu, 25 Feb 2021 23:57:27 -0500 2021-03-25T12:00:00-04:00 2021-03-25T13:30:00-04:00 Off Campus Location Department of Learning Health Sciences Livestream / Virtual LHS Collaboratory Logo
CCMB / DCMB Weekly Seminar (March 31, 2021 4:00pm) https://events.umich.edu/event/83395 83395-21369780@events.umich.edu Event Begins: Wednesday, March 31, 2021 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract:

Large, deeply phenotyped cohorts are reshaping the world of environmental epidemiology. Two such “big data” resources that are reshaping how we understand environmental health are electronic health records and human cohorts with genome-wide molecular phenotyping. Each provides a unique perspective that is moving the field closer towards “personalized” insights into environmental health risks. Here I will talk about a series of studies which utilize electronic health records and molecularly phenotyped cohorts to investigate vulnerable populations, gene-environment interactions, and epigenetic biomarkers of environmental sensitivity. Together these studies are helping us to understand environmental health risks in a new light.

Short bio:

Dr. Cavin Ward-Caviness is a Principal Investigator in the Public Health and Integrated Toxicology Division of the US Environmental Protection Agency. With a background in computational biology and environmental epidemiology, Dr. Ward-Caviness seeks to understand the environmental factors which influence health in vulnerable populations and the molecular mechanisms that influence environmental health risks. The Ward-Caviness lab uses a variety of “big data” approaches, and Dr. Ward-Caviness is the PI of the EPA CARES research resource, which allows researchers to study environmental health effects in vulnerable patient populations, e.g. individuals with heart failure, using large electronic health record databases. Dr. Ward-Caviness is also interested in how epigenetics and metabolomics can serve as an early indicator of adverse health effects from chemical and social environmental exposures and in particular how molecular biomarkers can give us insight into how the environment may accelerate the aging process and thus contribute to chronic disease. By integrating molecular and clinical data, Dr. Ward-Caviness seeks to understand environmental health as a way to advance personalized medicine and reduce health disparities.

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Livestream / Virtual Mon, 29 Mar 2021 15:15:11 -0400 2021-03-31T16:00:00-04:00 2021-03-31T17:00:00-04:00 Off Campus Location DCMB Seminar Series Livestream / Virtual
CCMB / DCMB Weekly Seminar Series (April 7, 2021 4:00pm) https://events.umich.edu/event/83241 83241-21320453@events.umich.edu Event Begins: Wednesday, April 7, 2021 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract: More than 3,000 new Marine recruits were studied prospectively during their initial Marine-mandated two-week quarantine and their subsequent basic training at Parris Island. The COVID Health Action Response for Marines (CHARM) studied completed 20,000 study visits and obtained more than 70,000 biosamples including pre- to post- SARS-CoV-2 infections in more than 1000 recruits. Serological, transcriptomic, and epigenetic analyses identify the response signature to SARS-CoV-2 infection in these largely asymptomatic young adults. Phylogenetic analysis and modeling provide insight into epidemiology and guidance for public health measures.

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Specialty: Neurology

Research Topics: Addiction, Apoptosis/Cell Death, Basal Ganglia, Bioinformatics, Brain, Cellular Immunity, Cerebral Cortex, Mathematical and Computational Biology, Multiple Sclerosis, Neuro-degeneration/protection, Receptors, Reproductive Biology, Signal Transduction, Theoretical Biology, Vaccine Development, Viruses and Virology

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Livestream / Virtual Tue, 23 Mar 2021 11:23:58 -0400 2021-04-07T16:00:00-04:00 2021-04-07T17:00:00-04:00 Off Campus Location DCMB Seminar Series Livestream / Virtual
CCMB / DCMB Weekly Seminar (April 14, 2021 4:00pm) https://events.umich.edu/event/83595 83595-21436485@events.umich.edu Event Begins: Wednesday, April 14, 2021 4:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract:
My lab's research involves the development and application of systems biology approaches—combining computation, machine learning, quantitative modeling, and experiments—to study the immune system in health and disease. Recent technological and computational advances allow comprehensive interrogation of multiple modalities (e.g., proteins, mRNAs, immune receptor sequences) in single cell resolution in the human population. Here I will highlight our work in the analysis human and single cell variations along the axes of early immune development, vaccination, and COVID-19. If time permits, I will also discuss the integration of tissue imaging, machine learning, and multiscale dynamical modeling of immune cell interactions to investigate the homeostatic regulation of autoreactive T cells.

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Biography: Dr. Tsang is a senior investigator in the NIH Intramural Research Program and leads a laboratory focusing on systems and quantitative immunology at the National Institute of Allergy and Infectious Diseases (NIAID). He also co-directs the Trans-NIH Center for Human Immunology (CHI) and leads its research program in systems human immunology. Dr. Tsang trained in computer engineering and computer science at the University of Waterloo and received his Ph.D. in biophysics from Harvard University. Dr. Tsang has worked as a software engineer and pursued systems biology research in both academia and industry including Rosetta Inpharmatics, Caprion Proteomics, MIT, and Merck Research Laboratories. Dr. Tsang has won several awards for his research, including NIAID Merit Awards for the development of a data reuse and crowdsourcing platform OMiCC and for leading a system biology study of human immune variability and influenza vaccination, which was selected as a top NIAID Research Advances of 2014. He currently serves as the founding chief editor of systems immunology for Frontiers in Immunology. He has served as a scientific advisor for a number of programs and organizations including ImmPort (the clinical and molecular data repository for NIAID), the Committee on Precision Medicine for the World Allergy Organization, the NIAID Modeling Immunity for Biodefense Program, the Allen Institute, the Immuno-Epidemiology Program at the National Cancer Institute, and the Human Vaccines Project.

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09

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Livestream / Virtual Wed, 07 Apr 2021 08:59:05 -0400 2021-04-14T16:00:00-04:00 2021-04-14T17:00:00-04:00 Off Campus Location DCMB Seminar Series Livestream / Virtual
Special Joint Seminar between DCMB, Mathematics, MIDAS, and Smale Institute (April 22, 2021 1:00pm) https://events.umich.edu/event/83615 83615-21491327@events.umich.edu Event Begins: Thursday, April 22, 2021 1:00pm
Location: Off Campus Location
Organized By: DCMB Seminar Series

Abstract:

The quest to understand consciousness, once the purview of philosophers and theologians, is now actively pursued by scientists of many stripes. This talk looks at consciousness from the perspective of theoretical computer science. It formalizes the Global Workspace Theory (GWT) originated by cognitive neuroscientist Bernard Baars and further developed by him, Stanislas Dehaene, and others. Our major contribution lies in the precise formal definition of a Conscious Turing Machine (CTM), also called a Conscious AI. We define the CTM in the spirit of Alan Turing’s simple yet powerful definition of a computer, the Turing Machine (TM). We are not looking for a complex model of the brain nor of cognition but for a simple model of (the admittedly complex concept of) consciousness. After formally defining CTM, we give a formal definition of consciousness in CTM. We then suggest why the CTM has the feeling of consciousness. The reasonableness of the definitions and explanations can be judged by how well they agree with commonly accepted intuitive concepts of human consciousness, the range of related concepts that the model explains easily and naturally, and the extent of its agreement with scientific evidence.

https://umich.zoom.us/j/95135773568

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Livestream / Virtual Wed, 14 Apr 2021 10:17:45 -0400 2021-04-22T13:00:00-04:00 2021-04-22T14:00:00-04:00 Off Campus Location DCMB Seminar Series Livestream / Virtual