Deep Learning for Genetics, Genomics and Metagenomics: Latest developments and New Directions
Videos from BIRS Workshop
Anshul Kundaje, Stanford University
Monday Jun 6, 2022 08:00 - 08:40
Deep learning oracles for genomic discovery
Jian Zhou, UT Southwestern Medical Center
Monday Jun 6, 2022 09:20 - 09:45
Sequence-based modeling of three-dimensional genome architecture from kilobase to chromosome scale
Julien Gagneur, Technical University of Munich
Monday Jun 6, 2022 09:45 - 10:10
A genetic algorithm with deep learning based guided mutations improves de novo peptide sequencing
Mathieu Blanchette, McGill University
Monday Jun 6, 2022 10:10 - 10:35
Learning from Evolution
Camille Rochefort-Boulanger, Université de Montréal
Monday Jun 6, 2022 14:20 - 14:45
Deep learning on genetic data with Diet Network and its application to a complex phenotype
James Zou, Stanford University
Monday Jun 6, 2022 14:30 - 15:10
Geometric Deep Learning for Multiplex Spatial Biology
Joseph Szymborski, McGill University
Monday Jun 6, 2022 14:45 - 15:10
RAPPPID: Deep, Regularised Protein-Protein Interaction Prediction that Generalises to Unseen Proteins
Jian Tang, HEC Montreal
Monday Jun 6, 2022 15:10 - 15:35
Geometric Deep Learning for Drug Discovery
Selin Jessa, McGill University
Monday Jun 6, 2022 17:00 - 17:25
Data-driven approaches to identify the origins of pediatric brain tumors
Jesse Islam, McGill
Monday Jun 6, 2022 17:20 - 17:50
Case-Base Neural Networks: survival analysis with time-varying, higher-order interactions
Wei Sun, Fred Hutchinson Cancer Center
Tuesday Jun 7, 2022 08:00 - 08:25
Biologically-informed neural networks for scRNA-seq data
Jingshu Wang, The University of Chicago
Tuesday Jun 7, 2022 08:25 - 08:50
Model-Based Trajectory Inference for Single-Cell RNA Sequencing Using Deep Learning with a Mixture Prior
Mingyao Li, University of Pennsylvania
Tuesday Jun 7, 2022 08:50 - 09:15
Deciphering tissue microenvironment by integrative analysis of spatial transcriptomics with histology images and single cells
Ritambhara Singh, Brown University
Tuesday Jun 7, 2022 09:30 - 10:10
Towards spatial and temporal modeling of gene regulation using deep learning
Hongyu Zhao, Yale University
Tuesday Jun 7, 2022 10:35 - 11:00
Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders
Jessica Li, McGill University
Tuesday Jun 7, 2022 13:00 - 13:40
An all-in-one statistical framework that simulates realistic single-cell omics data and infers cell heterogeneity structure
Hongkai Ji, Johns Hopkins Bloomberg School of Public Health
Tuesday Jun 7, 2022 13:40 - 14:05
Cross-modality prediction and imputation of functional genome on single cells
Sara Mostafavi, University of Washington
Tuesday Jun 7, 2022 14:20 - 14:45
Deep learning of immune cell differentiation
Nancy Zhang, University of Pennsylvania
Tuesday Jun 7, 2022 14:45 - 15:10
Deep neural networks for denoising and batch correction in single cell genomics
Rui Jiang, Tsinghua
Tuesday Jun 7, 2022 15:10 - 15:35
Deep learning for cell type identification based on single-cell chromatin accessibility data
Sriram Sankararaman, UCLA
Wednesday Jun 8, 2022 08:25 - 08:50
Interpretable deep learning for genomic discovery
Michael Hoffman, University Health Network/University of Toronto
Wednesday Jun 8, 2022 08:50 - 09:30
Reproducibility standards for machine learning in the life sciences
Jessica Gronsbell, University of Toronto
Wednesday Jun 8, 2022 09:45 - 10:10
Leveraging electronic health records for genetic research
Yue Li, McGill University
Wednesday Jun 8, 2022 10:10 - 10:35
Neural topic modeling of electronic health records
Kushal Dey, Harvard T.H. Chan School of Public Health
Thursday Jun 9, 2022 08:00 - 08:25
Evaluating the informativeness of deep learning annotations for human complex diseases
Valentina Boeva, ETH Zurich
Thursday Jun 9, 2022 09:05 - 09:30
Deciphering shared intratumor transcriptional heterogeneity of human tumors
Amin Emad, McGill University
Thursday Jun 9, 2022 09:30 - 10:10
Deep Learning in Cancer Precision Medicine
Pingzhao Hu, University of Manitoba
Thursday Jun 9, 2022 10:10 - 10:35
Deep learning-empowered breast cancer radiogenomics for precision medicine
Lei Sun, University of Toronto
Thursday Jun 9, 2022 12:30 - 12:55
Unusual suspects: Recent advances and new challenges in X chromosome-inclusive analyses
Xihong Lin, Harvard University
Thursday Jun 9, 2022 12:55 - 13:20
Estimation of the number of ancestry PCs using bulk eigenvalue matching analysis
Wei Pan, University of Minnesota
Thursday Jun 9, 2022 13:20 - 13:45
DeLIVR: A Deep Learning Approach to Testing for Non-linear Causal Effects in Transcriptome-Wide Association Studies
Gabriela Cohen-Freue, University of British Columbia
Thursday Jun 9, 2022 13:45 - 14:10
Scaling up Mendelian randomization for high-dimensional -omics data
Shuangning Li, Stanford University
Thursday Jun 9, 2022 14:30 - 14:55
Searching for Robust Associations with a Multi-Environment Knockoff Filter
Linxi Liu, University of Pittsburgh
Thursday Jun 9, 2022 14:55 - 15:20
Identifying putative causal loci in whole-genome sequencing studies via knockoff Statistics
Fengzhu Sun, USC
Thursday Jun 9, 2022 15:20 - 15:45
DeepLINK: Deep learning inference using knockoffs with applications to genomics
