Learning Meaningful Representations of Life (LMRL) Workshop @ ICLR 2026

Kristina Ulicna, Rebecca Boiarsky, Till Richter, Soo-Jeong Kim, Lazar Atanackovic, Jason Hartford, Romain Lopez, Thouis R Jones

Keywords: representation learning, meaningful embeddings, virtual cell, representations of life, model interpretability, explainable biology, RNA-sequencing, DNA-sequencing, genomics, transcriptomics, sequence modeling, foundation models, causal inference, morphological profiling, perturbation screening, cell phenotyping

TL;DR: LMRL workshop at ICLR 2026 will bring together experts to discuss the latest advances in learning and evaluating biological data representations, with a focus on causality, interpretability, and generating biological insights.

Abstract: Learning Meaningful Representation Learning (LMRL) Workshop 2026 aims to identify the key bottlenecks in the development of virtual cells. Virtual cells are an in silico representation of a cell’s behaviour and dynamics in both health and disease, with immense implications for research, diagnostics and therapeutic development. Building towards such a system begins with learning meaningful representations within individual modalities, which form the foundations for scaling the complex heterogeneous biological signals into a coherent model of a cell, and combining them into integrative models that capture biology’s complexity. LMRL 2026 highlights emerging directions for overcoming these challenges by focusing on four core ingredients - causality in biological systems, generative modelling, interpretable representations, and leveraging virtual cells for real-world impact. This workshop aims to catalyse the advances in how we learn meaningful representations by bringing together the AIxBio community around a shared scientific roadmap.

Submission Number: 41