Mola: sequence-to-distribution framework models single-molecule chromatin states
anshulkundaje · x · 2026-11-06
A University of Chicago team (Jian Zhou's group) posted a bioRxiv preprint introducing a sequence-to-distribution (S2d) framework that models the full probabilistic distribution of single-molecule chromatin states from DNA sequence alone, training a model called Mola. Unlike population-average deep learning models, Mola captures single-molecule heterogeneity and dynamics; the authors use it to quantify sequence-driven nucleosome regulation and reveal distinct modes of transcription factor engagement with nucleosomes.
Related event: Mola Models Single-Molecule Chromatin State Distributions from DNA Sequence(4 posts)→
More from Research
- Memorilla adds latent semantic memory to LLMs, positioned beyond RAG — StanfordAILab · 2026-11-06
- Team finds models trained for collaboration match frontier coding without explicit coding training — gharik · 2026-11-06
- NVIDIA open-sources Dory1.0-4B: looped latent-recurrent LLM beats Qwen3.5-9B on 12 benchmarks — ChengleiSi · 2026-11-06
- Rohit Krishnan maps "AGI institutions" experiments: 11 strong matches, zero at national scale — soumitrashukla9 · 2026-11-06
- Anneal V1 Core released: CPU-native low-parameter state-space architecture under CC0 — True-Beach1906 · 2026-11-06
- AI audit of 138 economics papers finds 2,035 errors, 69% of papers affected — panickssery · 2026-11-06