ICLR 2026PastGenomics
ICLR 2026 Workshop on Machine Learning for Genomics Explorations
MLGenX 2026
- Submission deadline
- Feb 9, 2026, 11:59 UTCOpenReview-synced 2026-02-09 11:59 UTC (as of 2026-06-23) — extensions on OpenReview are applied automatically; verify on the website.
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (82)
Fetched from OpenReview (v2) on 2026-06-10.
A CONVERSATIONAL MULTI-AGENT AI FRAMEWORK FOR INTEGRATED MULTI-OMICS ANALYSIS AND BIOMEDICAL DISCOVERY
Active Learning for Optimal Experimental Design in Alzheimer's Disease Drug Discovery: Prioritizing NAD+-Enhancing Therapeutic Analogs via Multi-Objective Bayesian Optimization
ACTIVEGENE: REWARD-FREE, HOMEOSTASIS- ALIGNED CONTROL FOR CLOSED-LOOP GENE REGULATION VIA ACTIVE INFERENCE
Addressing Instrument-Outcome Confounding in Mendelian Randomization through Representation Learning
Adversarial Genomic Sequences Could Evade Biosecurity Screening
Agentic Active Causal Discovery for Alzheimer's Disease Reversal: Closing the Genomic Experimental Loop
Agentic Orchestration of Drug Discovery ML Tools Under Partial Observability
Ancestry Inference with GNNs on IBD Graphs for Genetically Similar Populations
Attractome: From Theory to Generative Models for Cancer Dynamics and Control
Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models
Bayesian Rips Active Learning: Topology-Aware Acquisition for Rare Lineages
Beyond Mean Shifts: Predicting Distributional Responses to Unseen Genetic Perturbations
Beyond single-axis designs: multi-objective optimization for complex perturbation atlases
BIO-Distiller: Boosting Supervised Baselines by Distilling Biological Foundation Models
BioCOMPASS: Integrating Biomarkers into Transformer-Based Immunotherapy Response Prediction
Causal Field Theory: Mechanistic Interpretability for Spatio-Temporal Biological Systems
CAUSALPERT: GROUNDING LLM HYPOTHESES IN REGULATORY NETWORKS FOR GENE PERTURBATION PREDICTION
CELLTARNET: SINGLE-CELL PERTURBATION PREDICTION USING TRANSFORMER BASED NORMALIZING FLOW
CellxPert: Inference-Time MCMC Steering of a Multi-Omics Single-Cell Foundation Model for In-Silico Perturbation
ChatSpatial: Schema-Enforced Agentic Orchestration for Reproducible Spatial Transcriptomics Analysis
Conditional Monte Carlo Tree Diffusion for Designing Cell-Type-Specific and Biologically Faithful Regulatory DNA
Conditional Single-Cell RNA Generation: A Decoder Model and A Benchmark of Generative Models
CONSTRAINED LANGUAGE-GUIDED REFINEMENT FOR ZERO-SHOT SPATIAL ANNOTATION
Contrastive Alignment of Expression and Copy Number Highlights Dosage-Insensitive Genes in Cancer
CP-BG-1M: A Controlled Multi-View Benchmark for Density and Background Shortcuts in Morphology Profiling
D3LM: A Discrete DNA Diffusion Language Model for Bidirectional DNA Understanding and Generation
DC-W2S: Dual-Consensus Weak-to-Strong Training for Reliable Process Reward Modeling in Biological Reasoning
DELBERT: Fingerprint Language Modeling For Generalizable Hit Discovery in DNA-Encoded Libraries
Discrete Diffusion for Single-Cell Gene Expression Modeling
Dissecting and Steering Cell Identity in a Single-Cell Foundation Model Using Sparse Autoencoders
Distilling Genomic Models for Efficient mRNA Representation Learning via Embedding Matching
DNACHUNKER: Learnable Tokenization for DNA Language Models
DREAM-DNA: Controlled Design via Reasoning and Matched-flows for DNA
ECLIPSE: A Composable Pipeline for Predicting ecDNA Formation, Evolution, and Therapeutic Vulnerabilities in Cancer
ELISA: An Interpretable Hybrid Agent for Expression-Grounded Discovery in Single-Cell Genomics
Enigma: An Efficient Model for Deciphering Regulatory Genomics
Event Embedding of Protein Networks : Compositional Learning of Biological Function
Exploring Perturbation Effects on Transcriptional Dynamics with ContrastiveBiVI
From Edge Detection to Regulatory Logic Discovery: Residual Set Models for Exact Regulator Recovery in Gene Regulatory Networks
From k-mers to Genomic Foundation Models: Benchmarking COX1 Taxonomy under Extreme Class Imbalance
GenePT Revisited: Do Better Text Embeddings Make Better Gene Embeddings?
Generating and decoding methylated DNA with a Human Epigenetic Foundation Model
Genomic Next-Token Predictors are In-Context Learners
GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model
Hierarchical Multi-Omic CLIP for Missing-Modality Imputation & Transfer Learning in Blood Cancers
Interpretability Driven Evolutionary Approach for the Design of Biological Sequences
Investigation of Scaling Laws for Encoder-Decoder Protein Language Models
Joint Variable Selection in Proteomics Survival Models
Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction
Learning Perturbation Effects Through Contrastive Alignment of Transcriptomics and Textual Embeddings
LENS: LLM-based Enrichment of Nested Subclusters
LLM-BMC: Resolving Cell Type Ambiguity through Bayesian Integration of Biological Knowledge
LLM-Guided Retrieval for Prediction of Molecular Perturbation Responses
MapPFN: Learning Causal Perturbation Maps in Context
Modern Gene Finders: ab initio gene discovery benchmark with DNA language models
mRNABench: A curated benchmark for mature mRNA property and function prediction
Multimodal Latent Causal VAE for Joint Inference of Gene Regulatory and Protein Interaction Networks
Nexus: A Multi-Scale Simulator for Biological Control and Causal Discovery
Observation-Regime-Aware Bayesian Updates for Closed-Loop Scientific Agents
Optimizing Genomic Language Models for Efficient Training, Fine-Tuning, and Inference
PANGENSA: A graph-constrained machine learning framework for identifying antibiotic resistance determinants in bacterial pangenomes
PerturBERT: Learning Gene Co-Variation Embeddings from Perturbation Signatures
Predictive Performance is Often Insensitive to Feature Selection in High-Dimensional Biological Classification
RAPTORGraph: Graph-Based Pathway Modeling for Causal Discovery in Single-Cell Perturbations
REIGN: Robust Expected Information Gain for Navigating Adaptive Perturbation Screens
Rethinking Perturbation Prediction Baselines
SCOPES: Measuring Accuracy–Portability Trade-offs Across Microarray and RNA-Seq
SIMULTANEOUS LEARNING FROM BULK AND SINGLE-CELL EXPRESSION DATA WITH PERCEIVER-BASED MODELS
Single-Cell Concept Bottleneck Generative Models for Interpretable and Controllable Cellular Editing
Sparse Control of Disease-Aligned Gene Programs in Single-Cell Transcriptomics
Spatial Instrumental Variables for Causal Gene Regulatory Network Discovery from Spatial Transcriptomics
SplicedVAE: Learning Splicing Ratios from scRNA-seq to Enhance RNA Velocity and Cellular Trajectories
SQUINT: Spatial Quantization for Understanding and IN-painting Tissues
STAGE: A Foundation Model for Spatial Transcriptomics Analysis via Graph Embeddings with Hierarchical Prototypes
Structure-aware graph learning predicts RNA editability across tissues and species
SynthPert: Enhancing LLM Biological Reasoning via Synthetic Reasoning Traces for Cellular Perturbation Prediction
Taking The Easy Way Out: When Single-Cell Foundation Models Learn Shortcuts Instead of Biology
The Mechanistic Invariance Test: Genomic Language Models Fail To Learn Positional Regulatory Logic
TRUST-REGION SALIENCY-GUIDED LOCAL SEARCH FOR INTERPRETABLE SEQUENCE DESIGN AT FIXED EDIT BUDGETS
Uncertainty-Aware Biomarker Discovery for Alzheimer's Disease Reversal: Bridging Mouse Models and Human Translation with Conformal Prediction
VALIDATING INTERPRETABILITY IN SIRNA EFFICACY PREDICTION: A PERTURBATION-BASED, DATASET- AWARE PROTOCOL
VOUS: Variational Ornstein-Uhlenbeck Stochastics Linking Single-Cell Lineage Tracing with Dynamic Gene Expression