ICLR 2024PastGenomics
ICLR 2024 Workshop on Machine Learning for Genomics Explorations
MLGenX 2024
- Submission deadline
- Feb 10, 2024, 11:59 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (46)
Fetched from OpenReview (v2) on 2026-06-10.
A mechanistically interpretable neural-network architecture for discovery of regulatory genomics
AcceleratedLiNGAM: Learning causal DAGs at the speed of GPUs
Active learning to discover pairwise genetic interactions via representation learning
ADVANCING DNA LANGUAGE MODELS: THE GENOMICS LONG-RANGE BENCHMARK
Are Genomic Language Models All You Need? Exploring Genomic Language Models on Protein Downstream Tasks
BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments
Biologically Interpretable VAE with Supervision for Transcriptomics Data Under Ordinal Perturbations
Cell-Type Prediction in Spatial Transcriptomics Data using Graph Neural Networks
cellFlow: a generative flow-based model for single-cell count data
Contrastive Poincaré Maps for single-cell data analysis
DARKIN: A zero-shot classification benchmark and an evaluation of protein language models
Deep Learning and Direct Sequencing of Labeled RNA Captures Transcriptome Dynamics
Dirichlet Flow Matching with Applications to DNA Sequence Design
Disentanglement via Mechanism Sparsity by Replaying Realizations of the Past
DNA language models identify variants predictive across the human phenome
DNA-DIFFUSION: LEVERAGING GENERATIVE MODELS FOR CONTROLLING CHROMATIN ACCESSIBILITY AND GENE EXPRESSION VIA SYNTHETIC REGULATORY ELEMENTS
Drug Discovery with Dynamic Goal-aware Fragments
Enhancing generative perturbation models with LLM-informed gene embeddings
Evaluating predictive patterns of antigen specific B cells by single cell transcriptome and antibody repertoire sequencing
Evaluating Spatial Encoding Strategies for Cell Type Annotation with Spatial Omics Data
EvoSBDD: Latent Evolution for Accurate and Efficient Structure-Based Drug Design
Expanding Genomic Discovery: Causally-Inspired Neural Networks for Predicting Therapeutic Targets
Fine-tuning Protein Language Models with Deep Mutational Scanning improves Variant Effect Prediction
INTEGRATION OF GRAPH NEURAL NETWORK AND NEURAL-ODES FOR TUMOR DYNAMICS PREDICTION
Interpretable and Generalizable Graph Learning via Subgraph Multilinear Extension
IST-editing: Infinite spatial transcriptomic editing in a generated gigapixel mouse pup
Joint Embedding of Transcriptomes and Text Enables Interactive Single-Cell RNA-seq Data Exploration via Natural Language
Learning Drug Perturbations via Conditional Map Estimators
Multi-ContrastiveVAE disentangles perturbation effects in single cell images from optical pooled screens
Multi-Modal Contrastive Learning for Proteins by Combining Domain-Informed Views
Multi-Resolution Graph Diffusion
NICHEVI: A PROBABILISTIC FRAMEWORK TO EMBED CELLULAR INTERACTION IN SPATIAL TRANSCRIPTOMICS
Optimizing Genetically-Driven Synaptogenesis
Pairing interacting protein sequences using masked language modeling
Propensity Score Alignment of Unpaired Multimodal Data
Protein Representation Learning by Capturing Protein Sequence-Structure-Function Relationship
Recurrent memory augmentation of GENA-LM improves performance on long DNA sequence tasks
ResTran: A GNN Alternative to Learn A Graph with Features
ROBUST SYMBOLIC REGRESSION FOR NETWORK TRAJECTORY INFERENCE
Sample, estimate, aggregate: A recipe for causal discovery foundation models
sc-OTGM: Single-Cell Perturbation Modeling by Solving Optimal Mass Transport on the Manifold of Gaussian Mixtures
Scalable Amortized GPLVMs for Single Cell Transcriptomics Data
scvi-hub: A flexible framework for reference enabled single-cell data analysis
Season combinatorial intervention predictions with Salt & Peper
Unveiling Zero Shot Prediction for Gene Attributes Through Interpretable AI
Whole Genome Transformers for Gene Interaction Effects in Microbiome Habitat Prediction