large language model

GPTAnno: Ontology-tree-guided hierarchical cell type annotation based on GPT models for single-cell data

GPTAnno provides a hierarchical cell type annotation method using GPT models and ontology trees, improving accuracy across species and tissues by integrating clustering with ontology constraints.

GRNITE: Gene Regulatory Network Inference with Text Embeddings

GRNITE improves gene regulatory network inference by combining text embeddings and graph neural networks, enhancing AUROC and recall across methods.

Integrative spatial multi-omics reveals prognostic tumor niches in female genital tumors

The paper presents an integrative spatial multi-omics atlas of female genital tumors, identifying prognostic tumor niches and developing a model to predict their abundance from histopathology.

TissueNarrator: Generative Modeling of Spatial Transcriptomics with Large Language Models

This paper introduces a framework that uses large language models to generate and analyze spatial transcriptomics data, improving the simulation of cell behavior and prediction of interactions.

Comparative Assessment of Large Language Models for Microbial Phenotype Annotation

This study evaluates LLMs for microbial phenotype annotation, finding varied performance across models and traits, with confidence scores correlating with accuracy.

General-Purpose Large Language Models, such as DeepSeek V3.2, Have Evolved Protein Design Capabilities

This study shows that general-purpose LLMs like DeepSeek V3.2 can generate protein sequences with desired structures and improve solubility without biological training.

A Multi-Agent Approach to Generating Context-Rich Gene Sets

This paper introduces a multi-agent system using large language models to enhance gene set databases by incorporating biological context from PubMed abstracts.

Talk2Biomodels: AI agent-based open-source LLM initiative for kinetic biological models

Talk2Biomodels provides an AI platform for interacting with biological models using natural language, enhancing accessibility and FAIR principles.

AI for IACUC: Accurate Initial Assessment of Institutional Animal Care and Use Committee Protocols

This paper develops an AI system to improve IACUC protocol reviews by detecting submission errors, achieving 100% recall and high precision in identifying issues.

Genolator: A Multimodal Large Language Model Fusing Natural Language, Genomic, and Structural Tokens for Protein Function Interpretation

Genolator is a multimodal large language model that combines genomic and structural data with natural language to interpret protein function, outperforming existing models in accuracy.

Contrastive Learning Enables Epitope Overlap Predictions for Targeted Antibody Discovery

This paper develops computational methods to predict epitope overlaps in antibodies, showing improved accuracy using contrastive learning and demonstrating their utility in antibody discovery.

Agentic Lab: An Agentic-physical AI system for cell and organoid experimentation and manufacturing

Agentic Lab integrates AI agents with physical lab operations to improve reproducibility in cell and organoid research by autonomously generating protocols and analyzing experimental data.

Simulating Tandem Mass Spectra for Small Molecules using a General-Purpose Large-Language Model

The paper uses a large-language model to simulate tandem mass spectra for small molecules, bypassing the need for domain-specific training.

Can Lightweight LLM Agents Improve Spatial TranscriptomicsAnnotation?

This study evaluates lightweight LLM agents for improving spatial transcriptomics annotation using rule-based heuristics and multirole reasoning across multiple datasets.

Language may be all omics needs: Harmonizing multimodal data for omics understanding with CellHermes

CellHermes integrates multimodal omics data using a biological language model, achieving comparable or better performance than single-cell models across various tasks.

CellOntologyMapper: Consensus mapping of cell type annotation

CellOntologyMapper standardizes cell type annotations by mapping user-defined names to standardized ontological terms, improving data integration and analysis across diverse single-cell datasets.

Connecting a P300 speller to a large language model

This paper connects a P300 speller to a large language model to improve text generation accuracy and communication speed by leveraging contextual error correction.

A pre-trained large generative model for translating single-cell transcriptome to proteome

This paper introduces scTranslator, a pre-trained large generative model that translates single-cell transcriptomes to proteomes, showing accuracy and utility in multiple downstream tasks.

Variant Classification Using Proteomics-Informed Large Language Models Increases Power of Rare Variant Association Studies and Enhances Target Discovery

The paper improves rare variant association studies by using proteomics-informed large language models, identifying more gene-trait associations than existing methods.

IMMUNIA: A Multi-LLM Reasoning Agent for Immunoregulatory Surfaceome Discovery

IMMUNIA identifies immunoregulatory surfaceome genes using multi-LLM reasoning, highlighting novel stromal immune checkpoint regulators.

PLiCat: Decoding protein-lipid interactions by large language model

PLiCat uses large language models to classify protein-lipid interactions across eight lipid categories and predict lipid-binding sites and mutation impacts.

DeepADR: Multi-modal Prediction of Adverse Drug Reaction Frequency by Integrating Early-Stage Drug Discovery Information via Kolmogorov-Arnold Networks

DeepADR predicts adverse drug reaction frequency using early-stage data by integrating chemical, biological, and semantic information with a Kolmogorov-Arnold Network.

Assessing the Performance of LLMs in Multimodal Information Extraction for Biological Research: A Case Study on LLPS

This study evaluates a multimodal LLM workflow for extracting biological information from text and figures, achieving an F1-score of 0.847 using Gemini 2.5 Pro.

Tokenizing single-cell transcriptomes as a native language for large language models

This paper introduces CellTok, a method to convert single-cell transcriptomes into tokens for LLMs, enabling joint processing of cellular data and text.

FveOCRs - a database for open chromatin prediction in wild strawberry based on a large language model

The paper introduces FveOCRs, a database that predicts open chromatin regions in wild strawberry using a large language model, and allows predictions for any plant species.

Human-scATAC-Corpus: a comprehensive database of scATAC-seq data

The paper introduces Human-scATAC-Corpus, a database of human scATAC-seq data, and presents a foundation model for epigenomic analysis.

Deep learning for mass extinction detection on fossilized phylogenies: power, limitations, and lessons for simulation-based phylodynamic inference

This study evaluates a deep learning model for detecting mass extinctions in fossilized phylogenies, finding it slightly better than Bayesian methods but limited by simulation stochasticity and definition specificity.

GRASP: Gene-relation adaptive soft prompt for scalable and generalizable gene network inference with large language models

GRASP improves gene network inference using LLMs by adapting prompts to gene pairs, outperforming other strategies and recovering unannotated interactions.

Biomedical Hypothesis Explainability with Graph-Based Context Retrieval

The paper introduces a method to explain biomedical hypotheses using graph-based context retrieval and LLMs, with feedback loops to refine explanations.

Protein large language model assisted one-to-one gene homology mapping in cross-species single-cell transcriptome integration

The paper develops a protein large language model-based method to improve gene homology mapping in cross-species single-cell transcriptome integration, increasing homology pairs and identifying novel cell-type markers.