ICLR 2025 Past Other
Learning Meaningful Representations of Life (LMRL) Workshop at ICLR 2025
ICLR 2025 Workshop LMRL
- Submission deadline
- Feb 13, 2025, 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 (84)
Fetched from OpenReview (v2) on 2026-06-10.
-
2DE: a probabilistic method for differential expression across niches in spatial transcriptomics data
-
A pretrained SCVI model for 60,000 drug perturbation experiments in 100 million cells
-
Adaptive Discrete Tokenization of Electrocardiograms for Clinical Applications
-
AI Foundation Models for Personalized Health Monitoring: Learning Meaningful Representations of Metabolic Profiles
-
AI-Powered Virtual Tissues from Spatial Proteomics for Clinical Diagnostics and Biomedical Discovery
-
Benchmarking and optimizing organism wide single-cell RNA alignment methods
-
Benchmarking Sample Representations from Single-Cell Data: Metrics for Biologically Meaningful Embeddings
-
Beyond Schrödinger Bridges: A Least-Squares Approach for Learning Stochastic Dynamics with Unknown Volatility
-
Boosting Protein Graph Representations through Static-Dynamic Fusion
-
Bridging scales between chemical space and behavioral phenotype
-
Bridging Sequence and Kinetics: Utilizing Multi-scale Representations for Genome-Scale Metabolic Models
-
CardioPRIME: Cardiovascular Physiological Representation Integration With Multimodal Embeddings
-
CellCLIP - Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning
-
Cellular-Guided Graph Generative Model
-
Character-level Tokenizations as Powerful Inductive Biases for RNA Foundational Models
-
Curly Flow Matching for Learning Non-gradient Field Dynamics
-
Decision Tree Induction with Dynamic Feature Generation: A Framework for Interpretable DNA Sequence Analysis
-
Delta ECG: A Genetic Perspective
-
DiffGraphTrans: A Differential Attention-Based Approach for Extracting Meaningful Features of Drug Combinations
-
END-TO-END INTERPRETABLE GRAPH LEARNING FOR PATIENT CLASSIFICATION
-
Exploring Query-to-reference Mapping Challenges for Automated Single-Cell Atlas-based Diagnostics
-
Extending Prot2Token: Aligning Protein Language Models for Unified and Diverse Protein Prediction Tasks
-
Flexible Models of Functional Annotations to Variant Effects using Accelerated Linear Algebra
-
Fractional Brownian Bridges for Aligned Data
-
From Mechanistic Interpretability to Mechanistic Biology: Training, Evaluating, and Interpreting Sparse Autoencoders on Protein Language Models
-
From Medical Literature to Predictive Features: An Evidence-based Knowledge Graph Approach
-
Generalized Representation Learning for Multimodal Histology Imaging Data Through Vision-Language Modeling
-
GluFormer: Learning Generalizable Representations from Continuous Glucose Monitoring Data
-
GRASP: GRAph-Structured Pyramidal Whole Slide Image Representation
-
Guided Generation of B-cell Receptors with Conditional Walk-Jump Sampling
-
Hierarchical Mixture of Topological Experts for Molecular Property Prediction
-
Identifying Critical Phases for Disease Onset with Sparse Haematological Biomarkers
-
Implicit Neural Representations of Molecular Vector-Valued Functions
-
Integrating Protein Language Model and Active Learning for Few-Shot Viral Variant Detection
-
Interpretable Enzyme Function Prediction via Residue-Level Detection
-
Interpretable Self-Supervised Prototype Learning for Single-Cell Transcriptomics
-
Interpreting and Steering Protein Language Models through Sparse Autoencoders
-
Large Language Model is Secretly a Protein Sequence Optimizer
-
Latent Representation Encoding and Multimodal Biomarkers for Post-Stroke Speech Assessment
-
Learning a mechanical growth model of flower morphogenesis
-
Learning to Predict Ensembles of Protein Conformations from Molecular Dynamics Simulation Trajectories
-
Leveraging State Space Models in Long Range Genomics
-
Leveraging Transfer Learning and Multimodal Foundation Models for Antibiotic Discovery Against Data-Scarce Escherichia coli Strains
-
Ligand-Conditioned Binding Site Prediction Using Contrastive Geometric Learning
-
MeMDLM: De Novo Membrane Protein Design with Property-Guided Discrete Diffusion
-
Metabolically Constrained Neural Networks for Bioprocess Optimization
-
Metalorian: De Novo Generation of Heavy Metal-Binding Peptides with Classifier-Guided Diffusion Sampling
-
MODIS: Multi-Omics Data Integration for small and unpaired datasets
-
moPPIt: De Novo Generation of Motif-Specific Peptide Binders via Conditional Uniform Discrete Diffusion
-
Multi-Modal Disentanglement of Spatial Transcriptomics and Histopathology Imaging
-
Multi-Modal Representation learning for molecules
-
muPPIt: De Novo Generation of Mutant-Specific Peptide Binders via Conditional Uniform Discrete Diffusion
-
Mutagenic: An Embedding-Based Approach to Protein Masking for Functional Redesign
-
NOLAN: CONSTRUCTING GRAPH REPRESENTATION OF TISSUE STRUCTURE WITH SELF-SUPERVISED LEARNING
-
Non-invasive, label-free biochemical imaging of intact cerebral organoids via deep learning-enhanced Raman microspectroscopy
-
Omni-Mol: Exploring Universal Convergent Space for Omni-Molecular Tasks
-
On multi-scale Graph Representation Learning
-
Out-of-distribution evaluations of channel agnostic masked autoencoders in fluorescence microscopy
-
PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks
-
Phyla: Towards A Foundation Model For Phylogenetic Inference
-
Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction
-
Representation Learning for Distributional Perturbation Extrapolation
-
Roll-AE: A Spatiotemporal Invariant Autoencoder for Uncovering Neuronal Electrophysiological Patterns
-
RxRx3-core: Benchmarking drug-target interactions in high-content microscopy
-
Sampling Protein Language Models for Functional Protein Design
-
Self-supervised Learning for Encoding Between-Subject Information in Clinical EEG
-
Simulation-Free Structure Learning For Stochastic Dynamics
-
SOAPI: Siamese-guided generation of Off-Target-Avoiding Protein Interactions
-
Spatially-Informed Sampling Enables Accurate Prediction of Large-Scale Mutational Effects
-
STATE-SPACE-LIKE MODELS TO CALL COPY NUMBERS
-
Target localization in cell-based image analysis and disease diagnosis
-
Task-Driven Graph Neural Network Pre-Training: A Path to Robust EEG Representations in Motor Planning
-
Tensor-DTI: Enhancing Biomolecular Interaction Prediction with Contrastive Embedding Learning
-
To Bin or not to Bin: Alternative Representations of Mass Spectra
-
Towards Interpretable Protein Structure Prediction with Sparse Autoencoders
-
Towards Protein Sequence & Structure Co-Design with Multi-Modal Language Models
-
Towards Representation Learning for Phenotyping beyond Animal Pose Estimation
-
Transformer-Based Integrative Patient Representations from Single-Cell RNA Data
-
Transformers trained on proteins can learn to attend to Euclidean distance
-
Universally Applicable And Tunable Graph-Based Coarse-Graining For Machine Learning Force Fields
-
Unsupervised Deep Disentangled Representation of Single-Cell Omics with DRVI
-
Unsupervised Whole-Genome Representation Learning Captures Bacterial Phenotypes
-
Using Autoregressive-Transformer Model for Protein-Ligand Binding Site Prediction
-
Weakly Supervised Latent Variable Inference of Proximity Bias in CRISPR Gene Knockouts from Single-Cell Images