Toggle Poster Visibility
Sat Jul 24 05:45 AM -- 05:50 AM (PDT)
Opening Remarks
Sat Jul 24 05:50 AM -- 06:25 AM (PDT)
Invited talk 1 - Lessons from the Pandemic for Machine Learning and Medical Imaging
Sat Jul 24 06:25 AM -- 06:30 AM (PDT)
Invited Talk 1 Q&A
Sat Jul 24 06:30 AM -- 06:45 AM (PDT)
Contributed Talk 1 - Multigrate: single-cell multi-omic data integration
Sat Jul 24 06:45 AM -- 06:50 AM (PDT)
Contributed Talk 1 Q&A
Sat Jul 24 06:50 AM -- 06:55 AM (PDT)
Spotlight Set 1-1 | Statistical correction of input gradients for black box models trained with categorical input features
Sat Jul 24 06:55 AM -- 07:00 AM (PDT)
Spotlight Set 1-2 | Opportunities and Challenges in Designing Genomic Sequences
Sat Jul 24 07:00 AM -- 07:05 AM (PDT)
Spotlight Set 1-3 | pmVAE: Learning Interpretable Single-Cell Representations with Pathway Modules
Sat Jul 24 07:05 AM -- 07:10 AM (PDT)
Spotlight Set 1-5 | Deep Contextual Learners for Protein Networks
Sat Jul 24 07:10 AM -- 07:15 AM (PDT)
Spotlight Set 1-4 | Multimodal data visualization, denoising and clustering with integrated diffusion
Sat Jul 24 07:30 AM -- 07:31 AM (PDT)
Introduction for Session 2
Sat Jul 24 07:31 AM -- 07:56 AM (PDT)
Invited talk 2 - Anomaly detection to find rare phenotypes
Sat Jul 24 07:56 AM -- 08:00 AM (PDT)
Invited Talk 2 Q&A
Sat Jul 24 08:00 AM -- 08:15 AM (PDT)
Contributed Talk 2 - Light Attention Predicts Protein Location from the Language of Life
Sat Jul 24 08:15 AM -- 08:20 AM (PDT)
Contributed Talk 2 Q&A
Sat Jul 24 08:20 AM -- 08:25 AM (PDT)
Highlight 1 | Representation of Features as Images with Neighborhood Dependencies forCompatibility with Convolutional Neural Networks
Sat Jul 24 08:25 AM -- 08:30 AM (PDT)
Highlight 2 | VoroCNN: Deep Convolutional Neural Network Built on 3D Voronoi Tessellation of Protein Structures
Sat Jul 24 08:30 AM -- 08:35 AM (PDT)
Highlight 3 | DIVERSE: Bayesian Data IntegratiVE learning for precise drug ResponSE prediction
Sat Jul 24 08:35 AM -- 08:40 AM (PDT)
Highlight 4 | Spherical Convolutions on Molecular Graphs for Protein Model Quality Assessment
Sat Jul 24 08:40 AM -- 08:45 AM (PDT)
Highlight 5 | Data-driven Experimental Prioritization via Imputation and Submodular Optimization
Sat Jul 24 08:45 AM -- 08:50 AM (PDT)
Highlight 6 | Data Inequality, Machine Learning and Health Disparity
Sat Jul 24 08:50 AM -- 08:55 AM (PDT)
Highlight 7 | Deep neural networks identify sequence context features predictive of transcription factor binding
Sat Jul 24 09:00 AM -- 10:00 AM (PDT)
Poster Session 1 and Break
Sat Jul 24 10:00 AM -- 11:00 AM (PDT)
Poster Session 2 and Break
Sat Jul 24 11:00 AM -- 11:01 AM (PDT)
Introduction for Session 3
Sat Jul 24 11:01 AM -- 11:26 AM (PDT)
Invited talk 3 - Every Patient Deserves Their Own Equation
Sat Jul 24 11:26 AM -- 11:30 AM (PDT)
Invited Talk 3 Q&A
Sat Jul 24 11:30 AM -- 11:45 AM (PDT)
Contributed Talk 3 - Reconstructing unobserved cellular states from paired single-cell lineage tracing and transcriptomics data
Sat Jul 24 11:45 AM -- 11:50 AM (PDT)
Contributed Talk 3 Q&A
Sat Jul 24 11:50 AM -- 11:55 AM (PDT)
Spotlight Set 2-1 | Equivariant Graph Neural Networks for 3D Macromolecular Structure
Sat Jul 24 11:55 AM -- 12:00 PM (PDT)
Spotlight Set 2-2 | Viral Evolution and Antibody Escape Mutations using Deep Generative Models
Sat Jul 24 12:00 PM -- 12:05 PM (PDT)
Spotlight Set 2-3 | Multi-Scale Representation Learning on Proteins
Sat Jul 24 12:05 PM -- 12:10 PM (PDT)
Spotlight Set 2-4 | Immuno-mimetic Deep Neural Networks (Immuno-Net)
Sat Jul 24 12:10 PM -- 12:15 PM (PDT)
Spotlight Set 2-5 | Gene expression evolution across species, organs and sexes in Drosophila
Sat Jul 24 12:15 PM -- 01:15 PM (PDT)
Poster Session 3 and Break
Sat Jul 24 01:15 PM -- 01:16 PM (PDT)
Introduction for Session 4
Sat Jul 24 01:16 PM -- 01:41 PM (PDT)
Invited talk 4 - Learning from evolution
Sat Jul 24 01:41 PM -- 01:45 PM (PDT)
Invited Talk 4 Q&A
Sat Jul 24 01:45 PM -- 02:00 PM (PDT)
Contributed Talk 4 - A Bayesian Mutation-Selection Model of Evolutionary Constraints on Coding Sequences
Sat Jul 24 02:00 PM -- 02:05 PM (PDT)
Contributed Talk 4 Q&A
Sat Jul 24 02:05 PM -- 02:20 PM (PDT)
Closing Remarks & Awards Ceremony
Identifying systematic variation in gene-gene interactions at the single-cell level by leveraging low-resolution population-level data
Spherical Convolutions on Molecular Graphs for Protein Model Quality Assessment
Deconvolution of the T cell immune response using multi-modal learning
Integrating unpaired scRNA-seq and scATAC-seq with unequal cell type compositions
Viral Evolution and Antibody Escape Mutations using Deep Generative Models
Light Attention Predicts Protein Location from the Language of Life
Data Inequality, Machine Learning and Health Disparity
VEGN: variant effect prediction with graph neural network
pmVAE: Learning Interpretable Single-Cell Representations with Pathway Modules
Opportunities and Challenges in Designing Genomic Sequences
Graph attribution methods applied to understanding immunogenicity in glycans
Distance-Enhanced Graph Neural Network for Link Prediction
MultImp: Multiomics Generative Models for Data Imputation
APObind: A Dataset of Ligand Unbound Protein Conformations for Machine Learning Applications in De Novo Drug Design
Simultaneous Selection of Multiple Important Single Nucleotide Polymorphisms in Familial Genome Wide Association Studies Data
Deep neural networks identify sequence context features predictive of transcription factor binding
Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis
Data-driven Experimental Prioritization via Imputation and Submodular Optimization
Statistical correction of input gradients for black box models trained with categorical input features
NeuRL: Closed-form Inverse Reinforcement Learning for Neural Decoding
Prediction of RNA-protein Interactions Using a Nucleotide Language Model
Equivariant Graph Neural Networks for 3D Macromolecular Structure
Reference-free cell type annotation and phenotype characterisation in single cell RNA sequencing by learning geneset representations
Graph Representation Learning on Tissue-Specific Multi-Omics
Neural message passing for joint paratope-epitope prediction
Designing Interpretable Convolution-Based Hybrid Networks for Genomics
Reconstructing unobserved cellular states from paired single-cell lineage tracing and transcriptomics data
Gene expression evolution across species, organs and sexes in Drosophila
Effective Surrogate Models for Protein Design with Bayesian Optimization
Exploring the latent space of deep generative models: Applications to G-protein coupled receptors
MultiMAP: Dimensionality Reduction and Integration of Multimodal Data
Towards better understanding of developmental disorders from integration of spatial single-cell transcriptomics and epigenomics
DynaMorph: self-supervised learning of morphodynamic states of live cells
Representation of Features as Images with Neighborhood Dependencies forCompatibility with Convolutional Neural Networks
Representation learning of genomic sequence motifs via information maximization
TCR-epitope binding affinity prediction using multi-head self attention model
Multi-target optimization for drug discovery using generative models
Multimodal data visualization, denoising and clustering with integrated diffusion
VoroCNN: Deep Convolutional Neural Network Built on 3D Voronoi Tessellation of Protein Structures
Prot-A-GAN: Automatic Protein Function Annotation using GAN-inspired Knowledge Graph Embedding
Semi-supervised Deconvolution of Spatial Transcriptomics in Breast Tumors
Drug Repurposing using Link Prediction on Knowledge Graphs
Improving confident peptide identifications across mass spectrometry runs by learning deep representations of TIMS-MS1 features
Epiphany: Predicting the Hi-C Contact Map from 1D Epigenomic Data
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