large language model

TridentTCR: Predicting T Cell Receptor Specificity for Antigens and Autoimmune-Related Targets via a Topology-Aware Graph and Large Language Model

TridentTCR predicts TCR specificity for antigens and autoimmune targets using a graph neural network and large language model, achieving better classification and identifying cross-reactivity in type 1 diabetes.

Decoding Breast Cancer Heterogeneity via Multi-Omics Integration and Language Model-Based Interpretation

This paper develops a multi-omics pipeline using MOFA and LLMs to classify breast cancer subtypes, achieving higher accuracy than traditional models.

AI-HOPE-TGFbeta: A Conversational AI Agent for Integrative Clinical and Genomic Analysis of TGF-β Pathway Alterations in Colorectal Cancer to Advance Precision Medicine

AI-HOPE-TGFbeta is a conversational AI agent that integrates clinical and genomic data to analyze TGF-β pathway alterations in colorectal cancer, revealing population-specific patterns and clinical outcomes.

Harnessing Large Language Models for Structured Extraction of CYP-Substance Interactions from Biomedical Texts

This paper presents a framework using large language models to extract CYP-substance interactions from biomedical texts, achieving high recall and precision.

EZ-FRCNN: A Fast, Accessible and Robust Deep Learning Package for Object Detection Applications from Ethology to Cell Biology

EZ-FRCNN provides a user-friendly, offline deep learning tool for object detection in biological datasets, enabling applications from cell biology to ecological studies.

Biological Reasoning with Reinforcement Learning through Natural Language Enables Generalizable Zero-Shot Cell Type Annotations

This paper uses a large language model to perform zero-shot cell type annotations on scRNA-seq data, showing improved performance over existing models and providing interpretable biological rationales.

NiCLIP: Neuroimaging contrastive language-image pretraining model for predicting text from brain activation images

NiCLIP predicts cognitive tasks and domains from brain activation maps using a contrastive language-image model trained on neuroscientific articles.

Evaluating Large Language Models for Gene-to-Phenotype Mapping: The Critical Role of Full-Text Database Access

This study evaluates LLMs for gene-to-phenotype mapping, finding that models with dedicated databases achieve >80% accuracy and provide supporting literature.

A Systematic Multi-LLM AI Framework for Immunotherapy Biomarker Discovery and Target Identification

This paper introduces a multi-LLM framework to identify immunotherapy biomarkers from Ig-domain-containing genes using multi-omics data and structured prompts.

Multi-agent AI System for High Quality Metadata Curation at Scale

This paper introduces a multi-agent AI system using large-language models to curate high-quality metadata from biomedical datasets, achieving high recall and scalability.

PhenoGraph: A Multi-Agent Framework for Phenotype-driven Discovery in Spatial Transcriptomics Data Augmented with Knowledge Graphs

PhenoGraph automates phenotype-driven analysis of spatial transcriptomics data using a multi-agent system and biological knowledge graphs to improve interpretability and efficiency.

GeneChat: A Multi-Modal Large Language Model for Gene Function Prediction

GeneChat predicts gene function from DNA sequences using a multi-modal large language model, outperforming GPT-4o on standard metrics.

DeepSeek as the paradigm shift in rare disease diagnosis-the power of a fully automated genetic variant classification system

This study develops a fully automated genetic variant classification framework using LLMs, achieving high sensitivity and 100% specificity in interpreting ACMG rules and matching human curators on curated variants.

HEAL-KGGen: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Genetic Biomarker-Based Medical Diagnosis

HEAL-KGGen improves LLM performance in genetic diagnostics by integrating a knowledge graph with a multi-agent framework for multi-omics data interpretation.

RPcontact: Improved prediction of RNA-protein contacts using RNA and protein language models

RPcontact predicts RNA-protein contacts using RNA and protein language models, outperforming existing methods on contact prediction.

Human intention inference with a large language model can enhance brain-computer interface control: A proof-of-concept study

This paper demonstrates that integrating large language models with neural and oculomotor signals improves BCI control accuracy for selecting arbitrary posts in a social networking service.

Biomni: A General-Purpose Biomedical AI Agent

An LLM agent with a literature-mined environment of 150 tools, 105 packages and 59 databases plans in code, matching reported human accuracy on database and sequence question answering.

LlamaAffinity: A Predictive Antibody Antigen Binding Model Integrating Antibody Sequences with Llama3 Backbone Architecture

LlamaAffinity predicts antibody-antigen binding affinity using a Llama 3 backbone and antibody sequences from the OAS database, achieving high accuracy and efficiency.

KODA: Agentic Framework for Microbiome Drug Target Discovery

KODA uses LLMs and knowledge graphs to identify antimicrobial drug targets in the gut microbiome by analyzing metabolic pathways and microbial interactions.

RAPID: Reliable and efficient Automatic generation of submission rePortIng checklists with large language moDels

RAPID is an automated tool using large language models to generate medical reporting checklists, achieving high accuracy on CONSORT and CONSORT-AI datasets.

PerTurboAgent: A Self-Planning Agent for Boosting Sequential Perturb-seq Experiments

PerTurboAgent predicts gene panels for sequential Perturb-Seq experiments using an LLM-based agent, outperforming existing strategies in identifying phenotypically impactful genes.

Artificial Intelligence Agent: AI-HOPE-TP53 Enables Pathway-Centric Analysis of TP53-Driven Molecular Alterations in Colorectal Cancer Precision Oncology

AI-HOPE-TP53 analyzes TP53-driven molecular alterations in colorectal cancer using natural language queries and clinical-genomic data to support precision oncology.

Trends Assessment of the Genetic Mutation Induced Protein-Protein Interaction Variation via Protein Large Language Driven Method

This paper introduces TAPPI, a deep learning framework that assesses trends in protein-protein interaction variations caused by genetic mutations, achieving state-of-the-art performance and biological relevance.

Releasing the octoPus, an open-source digital tool to promote Integrated Pest Management

The paper introduces octoPus, an open-source digital tool for Integrated Pest Management, which simulates grapevine downy mildew risk using multiple models and provides risk assessments and user messages.

An autonomous AI agent for universal behavior analysis

BehaveAgent automates cross-species behavior analysis from video input using multimodal AI without manual intervention or retraining, enabling zero-shot reasoning and end-to-end analysis.

Building A Unified Model for Drug Synergy Analysis Powered by Large Language Models

This paper introduces BAITSAO, a unified model for drug synergy prediction using large language models, showing improved performance over existing methods.

Advancing Plant Metabolic Research By Using Large Language Models To Expand Databases And Extract Labelled Data

This paper evaluates using large language models to expand plant metabolic databases by identifying enzyme-product pairs, compound-species associations, and transcribing table images with high accuracy.

Automating AI Discovery for Biomedicine Through Knowledge Graphs And LLM Agents

This paper introduces a framework combining knowledge graphs and LLM agents to automate biomedical discovery by identifying relevant pathways, designing AI predictors, and proposing wet-lab experiments.

scGenAI: A generative AI platform with biological context embedding of multimodal features enhances single cell state classification

scGenAI improves single-cell state classification by integrating multimodal biological features and LLMs, outperforming traditional methods in cell type prediction and AML malignant cell state identification.

Beyond Natural Antibodies: Scaffold-Based Generation of Novel Anti-3CLpro Nanobody Nb01

This paper develops a nanobody Nb01 targeting SARS-CoV-2 3CLpro, showing broad-spectrum neutralizing activity against multiple variants with higher affinity than existing nanobodies.