AI and the World's Hard Problems ─ Life sciences
Science and scholarship advanced by AI, by discipline
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LitGene: an interpretable transformer model integrating text and ontology for gene representation learning
Gene representation learning often overlooks the scientific literature, which can bias results against understudied genes. LitGene is an interpretable transformer that integrates text with Gene Ontology annotations through contrastive learning. It achieved competitive accuracy on eight protein benchmarks, zero-shot predicted GO terms, KEGG pathways and gene-disease links, and flagged ANGPTL6 as a candidate gene for idiopathic pulmonary fibrosis, supported by differential expression in independent samples.
Spatiotemporal AI-based modeling of gene regulation and variant effects in the developing human brain using Seal
The study addresses how genetic variation shapes gene regulation in the developing human brain, where data are limited. The authors built Seal, an interpretable AI transfer learning framework that predicts gene expression and variant effects across 26 regions, 30 cell types and 7 developmental stages. In autism whole-genome data, Seal found a significant burden of de novo regulatory variants in transient fetal excitatory neurons.
Hybrid modeling for improved structure prediction of large protein complexes
The study aims to predict the structures of large protein complexes. The authors developed HDOCK-Multimer (HDM), an automated framework that combines ab initio docking with combinatorial assembly. Across three benchmarks, HDM was more accurate and robust than state-of-the-art assembly and deep learning-based methods for large protein assemblies.
AI model decodes the language cells use to communicate
During embryonic development, cells receive chemical signals that switch genes on or off and determine their roles. Tracing the sequence of signals a cell has received has been hard, because signaling pathways were assumed to differ widely across cell types. MIT and Whitehead Institute researchers developed a computational framework to address this, which could help accelerate stem cell engineering, improve disease models, and inform cancer treatments.
How artificial neural networks build their own learning machinery
A group of randomly connected units can reorganize into an effective information-processing network when given simple rules for when and how connections should change. The MIT team argues that an artificial network's ability to organize itself, without a developer's guidance, is a key test of biological plausibility. The researchers caution that such networks may not offer much insight into biological learning.
Claude discovers a novel enzyme system
Anthropic's new life sciences lab uses Claude to explore DNA datasets for uncharacterized protein families. In early results, with only high-level direction from scientists, Claude agents found a novel enzyme system with unknown function and properties reminiscent of CRISPR. The work aims to generate hypotheses at scale and test them in the lab.
NiCLIP: neuroimaging contrastive language-image pretraining model for predicting text from brain activation images
NiCLIP is an AI model that decodes cognitive tasks, concepts and domains from brain activation patterns. It was trained on over 23,000 articles and reportedly outperforms traditional decoders at group-level decoding. It predicted cognitive tasks accurately from Human Connectome Project group-level maps, but showed limitations with noisy subject-level maps.
ETSAM: effectively segmenting cell membranes in cryo-electron tomograms
Cell membranes are hard to distinguish in noisy cryo-electron tomography volumes. The authors present ETSAM, a two-stage method that fine-tunes the Segment Anything Model 2 (SAM2) to segment membranes, trained on 83 experimental and 28 simulated tomograms. On 17 experimental test tomograms, it outperformed other deep learning methods with high sensitivity and better precision.
Tangermeme: a toolkit for understanding cis-regulatory logic using deep learning models
Deep learning models can predict genomic data well, but the harder step is using them for biological discovery. This paper describes tangermeme, a highly optimized toolkit for the parts of genomic deep learning other than the model itself. It shows how tangermeme can distill the cis-regulatory patterns learned by models into human-interpretable insights.
Generalizable perturbation prediction
Cell perturbation data reveal causal regulatory relationships, but such data are often scarce, which motivates methods that predict responses in one context from others. The authors developed State, a machine-learning model that combines a transformer-based state transition model for single-cell perturbation effects with a pretrained cell-embedding model. It was trained on perturbation data from over 100 million cells and observational data from 167 million cells, and the team also built Cell-Eval, an evaluation framework for comparing models.
Accurate and well-powered case–control analysis of spatial molecular data
Existing methods for spatial molecular data often use restrictive assumptions that can miss important disease signals. VIMA, a deep-learning method, uses an ensemble of variational autoencoders to summarize small tissue patches and identify microniches whose abundance correlates with case or control status. In simulations it was well calibrated and powerful, and applied to datasets spanning three diseases it recovered known biology and identified new spatial features of disease.
ProFormer: generalizable classification of single-cell and plasma proteomes using deep learning
Current proteomic workflows have low throughput and require extensive data processing, which slows sample classification. ProFormer is a transformer-based deep-learning pipeline that takes MS1-level peptide ion features directly from mass spectrometry data. It outperformed traditional machine learning and CNNs across 14 architectures and classified single-cell proteomes by cell type, cycle stage, and differentiation trajectory, as well as patient disease status from plasma proteomes.
CellART: a unified framework for extracting single-cell information from high-resolution spatial transcriptomics
High-resolution spatial transcriptomics platforms either capture sparse transcript counts per spot or measure only a limited number of genes, which makes single-cell information hard to extract. CellART combines staining images, spatial transcriptomics data and single-cell RNA-seq references, using deep learning and probabilistic modelling to segment cells and annotate cell types simultaneously. The authors report that it is efficient, generalizable and robust, and that its outputs are compatible with widely used community tools.
Introducing Quine: An AI research system designed for the complexity of biology
Microsoft Research's Quine is an early-stage effort to build a multimodal world model of biology and an interactive harness that connects models, scientific tools, literature, and researchers, helping scientists prioritize hypotheses before lab testing. In collaboration with researchers at the Broad Institute of Harvard and MIT, the team used it to prioritize compounds predicted to drive therapeutic tumor-state shifts and validated several top-ranked candidates across multiple wet-lab assays. The system is experimental, intended for research only, and its outputs require review by qualified researchers.