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