large language model

GEO-BERT: A Retrieval augmented LLM Framework for Intelligent Dataset Retrieval from the Gene Expression Omnibus

GEO-BERT improves dataset retrieval from GEO by using a retrieval-augmented LLM framework with a hierarchical hybrid field-aware system, enhancing semantic search and traditional search performance.

Enrich.AI: An Agentic AI Platform for Interactive GeneSet Enrichment Analysis and Biological Interpretation

Enrich.AI provides an AI-driven platform for gene set enrichment analysis and biological interpretation, offering interactive visualizations and literature-based insights.

RNASeek: A Cross-Phyla Generative Foundation Model for Multipurpose RNA Modeling and Reinforcement Learning-Based Design

RNASeek is a generative foundation model for RNA sequence design and functional prediction, using reinforcement learning to optimize RNA properties like stability and cleavage activity.

Automating scientific annotations for open transcriptomic profiles via multi-stage agents

GEOMeta automates metadata curation for GEO transcriptomic data, generating standardized annotations for 600,000 human bulk RNA-seq samples and benchmarking LLMs for annotation quality.

Automated generation of a gene perturbation transcriptomic atlas using large language models

The paper develops an automated pipeline using large language models to generate a gene perturbation transcriptomic atlas from public data, achieving high precision and recall in identifying perturbation groups.

BioMaster: Multi-agent System for Automated Bioinformatics Analysis Workflow

BioMaster automates complex bioinformatics workflows using a multi-agent system, improving accuracy, efficiency, and scalability across tasks like RNA-seq and Hi-C processing.

Detailed curation of biological samples and experimental designs for genomics using LLM-supported agentic workflows

The paper describes a software tool using LLMs to automate data curation for genomics, achieving performance near human curators with lower cost and higher speed.

LLM-powered Functional Gene Set Summarization with genesetGPT

genesetGPT is an LLM-based framework that improves gene set annotation by using curated biological context and iterative prompt design to summarize heterogeneous gene sets.

IOBRpy enables agentic multi-omics decoding of anti-tumor immunity

Python toolkit chains RNA-seq quantification, six-method tumour microenvironment (TME) profiling, SpecHLA typing and TRUST4 repertoire analysis with AI agents; TME outputs match R implementations (Spearman 0.986 to ~1.0).

Interpretable variant effect prediction from genomic foundation model embeddings

A low-rank covariance probe on Evo 2 embeddings predicts ClinVar pathogenicity across SNVs and indels, while annotation probes plus an LLM produce mechanistic hypotheses; scores released for 4.2 million variants.

The Language of Cancer: Decoding Cancer Signatures with Language Models and cfRNA Sequencing

GeneLLM uses language models to detect cancer from cfRNA sequences, identifying novel biomarkers from unannotated regions with improved accuracy.

Benchmarking large language models for cell-free RNA diagnostic biomarker discovery

This study benchmarks large language models for disease classification using plasma cell-free RNA profiles, showing their potential in gene-panel design and end-to-end modeling for diagnostics.

An LSEC-focused computational drug repurposing platform for liver fibrosis: Identification of vorinostat and other LSEC-protective candidates

This paper develops a computational platform to repurpose drugs for liver fibrosis by focusing on LSECs, identifying vorinostat and other candidates that protect LSECs.

Interpreting Omics Data Analysis with Large Language Models for Disease Target and Drug Discovery

This paper introduces a framework combining large language models with omics data analysis to prioritize disease targets and drug strategies, validated in Alzheimer's and pancreatic cancer.

ConvergeCELL: An end-to-end platform from patient transcriptomics to therapeutic hypotheses

ConvergeCELL translates patient transcriptomics into therapeutic hypotheses by integrating patient representation, interpretability, and large language models, outperforming baselines in gene recovery and classification.

A Pilot Evaluation of Open-Weight Large Language Models for Screening RNA-seq Metadata in Public Databases

The paper evaluates open-weight large language models for screening RNA-seq metadata in public databases, finding they significantly improve classification performance over keyword searches.

Metappuccino: Large Language Model-driven Reconstruction of Sequence Read Archive Metadata for Cancer Research

Metappuccino uses a fine-tuned large language model to enrich metadata from RNA-seq datasets, improving accuracy and consistency for cancer research.

Transcriptome-based cell type assignment for kidney cell culture models

The paper develops a transcriptome-based method to match bulk RNA-seq data from kidney cell lines to single-cell reference datasets, identifying accurate cell type identities and evaluating method performance.

Integrating 730,947 exome sequences with clinical literature improves gene discovery

gnomAD v4 (730,947 exomes) brings LOFTEE-2 loss-of-function annotation (0.90 precision, strict mode), missense-aware constraint, and LLM-mined literature evidence combined with constraint to rank disease genes.

Leveraging Large Language Models to Extract Prognostic Pathology Features in Ewing Sarcoma

This paper uses large language models to extract prognostic histologic features from Ewing sarcoma pathology reports, identifying NSE and S100 as significant biomarkers.

Literature-derived, context-aware gene regulatory networks improve biological predictions and mathematical modeling

The paper develops a framework to assign context-dependent weights to gene regulations from literature, improving biological predictions and mathematical modeling of disease-specific networks.

Clinical and Cross-Domain Validation of an LLM-Guided, Literature-Based Gene Prioritization Framework

The paper validates an LLM-guided gene prioritization framework for sepsis and tests its generalizability to obesity and infection using clinical and proteomic data.

Phenotype-Guided In Silico Molecular Generation Using Large Language Models

GEMGen generates small molecules from transcriptomic data to phenocopy gene knockdown effects and identify novel inhibitors.

Application of large language models to the annotation of cell lines and mouse strains in genomics data

This paper evaluates using GPT-4o to annotate mouse strains and cell lines in genomics data, achieving 77% and 59% accuracy, and identifies manual curation errors.

DEEPGEOSEARCH: LLM-POWERED SCHEMALESS RETRIEVALFOR BIOMEDICAL DATA DISCOVERY

DeepGEOSearch improves biomedical dataset discovery by using an LLM-powered schema-less retrieval system that matches natural-language queries to datasets with high precision and recall.

SigSpace: an LLM-based agent for drug responsesignature interpretation

SigSpace uses an LLM to interpret drug response signatures from cancer cell lines, generating summaries that outperform random baselines in matching to drug identity or mechanism of action.

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.

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.

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.

Base-editing a single missense mutation in A20 enhances CAR-T cell efficacy

This study uses base-editing to inactivate a missense mutation in A20ZF7, enhancing CAR-T cell efficacy by reducing exhaustion and improving anti-tumor responses.