NIH.AI
►NIH.AI Workshop on Applications of Machine Learning for Next Generation Sequencing & Drug Data
NIH.AI Workshop on Applications of Machine Learning for Next Generation Sequencing & Drug Data
Wednesday, October 23, 1-5 PM
Workshop Recording: CLICK to view
**Workshop presentations are listed in the agenda below.
Description:
Hosted by NIH.AI, this highly interactive workshop brought together members of the Intramural Research Program as well as members from the Department of Energy who are working on the cellular level pilot of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) Collaboration. The workshop included targeted presentations with open discussion among peers and hands-on demonstrations across disciplines.
**Workshop presentations may be found in the below agenda.
Goals:
-
Educate the NIH community on the applications of machine learning for next generation sequencing and drug data.
-
Foster collaboration between workshop attendees.
Agenda and Presentations:
Click Here for the cellular level pilot GitHub repo
|
1 - 1:40 pm |
Overview of Problems, Data Sets, Skills, and Resources Speaker: Arvind Ramanathan Presentation: Here Video: Here |
|
1:40 – 2:10 |
Speaker: Fangfang Xia Presentation: Here Video: Here @ Minute:55 References: Articles in deep learning and precision medicine |
|
2:15 - 2:20 |
Break |
|
2:20 - 2:55 |
Speaker: Fangfang Xia |
|
2:55 - 3:10 |
Break and discussion |
|
3:10 - 4:45 |
Representations of Molecular and Genetic Information for Dose Response Models Speaker: Austin Clyde Presentation: Here Video: Here @ 2h:30m References: |
|
3:45 - 3:50 |
Break |
|
3:50 - 4:25 |
Virtual Drug Screening with Deep Learning Speaker: Austin Clyde |
|
4:25 - 5:00 |
Open Discussion + Survey |
|
5:00 |
Adjourn |
Speakers:
Arvind Ramanathan, Ph.D. is a computational biologist in the data science and learning division at Argonne National Laboratory. His research focuses on the use of multi-scale modeling, simulations, and experiments for studying how intrinsically disordered proteins control complex signaling functions within cells. His research also focuses on building scalable statistical inference methods for integrating sparse experimental data with high dimensional data from simulations.
Fangfang Xia, Ph.D. is a computer scientist at the Data Science and Learning Division of Argonne National Laboratory. His research combines genomics, machine learning and high performance computing with particular interests in genome assembly, annotation, modeling and drug response prediction.
Austin Clyde is a computer science Ph.D. student at the University of Chicago. His research area is in deep learning and computational drug discovery. His current work is primarily de-novo molecule generation and accelerated virtual screening.
Questions?
Please contact either George Zaki (george.zaki@nih.gov) or Miles Kimbrough (miles.kimbrough@nih.gov).
Hosted by NIH.AI