Machine Learning in Genomics: Tools, Resources, Clinical Applications and Ethics

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Date/Time
Date(s) - 13 Apr 2021 until 14 Apr 2021
8:00 AM - 1:00 PM

Location


Machine Learning in Genomics: Tools, Resources, Clinical Applications and Ethics

Event Details

The NHGRI Genomic Data Science Working Group of the National Advisory Council for Human Genome Research is hosting the Machine Learning in Genomics virtual workshop on April 13 – April 14, 2021.

The workshop has a capacity of 1000 participants in Zoom. Anyone joining after the limit is reached will be redirected to a livestream of the workshop.

Recordings will be made available following the meeting on this webpage.

Day 1 Agenda  |  Day 2 Agenda

Organizing Committee

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Workshop Registration 

Registration is free and open to all.

Additional Information

The primary purpose of the workshop is to stimulate discussion around the opportunities and obstacles underlying the application of machine learning (ML) methods to basic genome sciences and genomic medicine, to define the key scientific topic areas in genomics that could benefit from ML analyses and NHGRI’s unique role at the convergence of genomic and ML research. 

The data-intensive fields of genomics and ML are in an early stage of convergence. This workshop will include a combination of lectures from ML, genomics, and ethics researchers with substantial time set aside for virtual Q&A sessions in which all attendees, irrespective of expertise and background, are encouraged to participate. Topics of interest within genomics cover the full spectrum of basic and clinical research. The workshop will also focus on the ethical aspects of ML applications to genomics data. ML scientists without any connection to genomics are as welcome to join as those already applying their analytical methods to genomic data. 

 

Day 1 Agenda

  • April 13, 2021
    11:00 a.m. – 5:00 p.m. EDT
  • 11:00 a.m. – WelcomeCo-chairs:
    Trey Ideker, Ph.D., University of California San Diego
    Mark Craven, Ph.D., University of Wisconsin

    Speaker: 
    Eric Green, M.D., Ph.D., Director, National Human Genome Research Institute

  • Keynote Session: What are the opportunities and challenges for ML in genomics research?

    Moderator: 
    Shannon McWeeney, Ph.D., Oregon Health and Sciences University

  • 11:10 a.m. – Eric Topol, M.D., Scripps Research
    Genomics in the Machine Learning Space

    11:40 a.m. – Brad Malin, Ph.D., Vanderbilt University Medical Center
    Challenges and Opportunities for Machine Learning in Genomics

    12:10 p.m. – Q&A Session

  • 12:40 p.m. – Break
  • Session 1: Algorithm development and machine learning approaches in genomics

    Moderators: 
    Trey Ideker, Ph.D., University of California San Diego
    Anthony Philippakis, M.D., Ph.D., Broad Institute

  • 1:00 p.m. – Jian Peng, Ph.D., University of Illinois at Urbana-Champaign
    Machine learning algorithms for structural and functional genomics

    1:25 p.m. – Sara Mathieson, Ph.D., Haverford College
    Automatic evolutionary inference using Generative Adversarial Networks

    1:50 p.m. – Christina Leslie Ph.D., Memorial Sloan-Kettering Cancer Center
    The 3D genome and predictive gene regulatory models

    2:15 p.m. – Q&A Session

  • 2:45 p.m. – Break
  • Session 2: Ethical, Legal and Social Implications (ELSI) of machine learning in genomics

    Moderators:
    Dave Kaufman, Ph.D., NHGRI
    Eimear Kenny, Ph.D., Icahn School of Medicine at Mount Sinai

  • 3:10 p.m. – Pamela Sankar, Ph.D., University of Pennsylvania
    Machine learning: broadening the scope of ethical questions

    3:35 p.m. – Varoon Mathur, AI Now Institute
    Considerations for building ethical and socially responsible AI systems in Health Care

    4:00 p.m. – Danton Char, M.D., Stanford University
    Identifying and Anticipating Ethical Challenges with Machine Learning for Genomics

    4:25 p.m. – Q&A Session

  • 4:55 p.m. – Day 1 Wrap-up

    Co-chairs:
    Trey Ideker, Ph.D., University of California San Diego
    Mark Craven, Ph.D., University of Wisconsin

  • 5:00 p.m. – Adjourn
 

Day 2 Agenda

  • April 14, 2021
    11:00 a.m. – 4:00 p.m. EDT
  • 11:00 a.m. – Day 2 Opening

    Co-chairs:
    Trey Ideker, Ph.D., University of California San Diego
    Mark Craven, Ph.D., University of Wisconsin

  • Session 3: Data and resource needs for machine learning in genomics

    Moderators:
    Christina Leslie, Ph.D., Memorial Sloan Kettering Cancer Center
    Mark Craven, Ph.D., University of Wisconsin

  • 11:10 a.m. – Alexis Battle, Ph.D., Johns Hopkins University
    Integrative machine learning for regulatory genomics

    11:35 a.m. – Anshul Kundaje, Ph.D., Stanford University
    Machine learning for genomic discovery

    12:00 p.m. – Gregory Cooper, M.D., Ph.D., University of Pittsburgh
    Personalized Causal Machine Learning Using Genomic Data

    12:25 p.m. – Q&A Session

  • 12:55 p.m. – Break
  • Session 4: Machine learning in clinical genomics

    Moderators:
    Casey Overby Taylor, Ph.D., Johns Hopkins University
    Eric Boerwinkle, Ph.D., University of Texas Health Science Center

  • 2:00 p.m. – Su-In Lee, Ph.D., University of Washington
    Explainable AI for cancer precision medicine

    2:25 p.m. – Sriram Sankararaman, Ph.D., University of California Los Angeles
    Machine Learning for large-scale genomics

    2:50 p.m. – Russ Altman, M.D., Ph.D., Stanford University
    Deep learning to predict the impact of rare variation in drug metabolism genes

    3:15 p.m. – Q&A Session

  • 3:45 p.m. – Day 2 Wrap-up

    Co-chairs:
    Trey Ideker, Ph.D., University of California San Diego
    Mark Craven, Ph.D., University of Wisconsin

  • 4:00 p.m. – Adjourn