large language model

vardictcpp: A C++ reimplementation of VarDict variant caller

An AI-assisted C++17 port of VarDictJava gives byte-identical calls on five exome samples while running 3.7-4.8 times faster on one core and needing 8-17 times less memory.

Large language models unlock the ecology of species interactions

This paper uses large language models to extract species interaction data from citizen science comments, expanding ecological data coverage and enabling new ecological research.

FragDockRL: A Reinforcement Learning Method for Fragment-Based Ligand Design via Building Block Assembly and Tethered Docking

FragDockRL uses reinforcement learning to design ligands by assembling building blocks and tethered docking, outperforming random search on three protein targets.

An fMRI dataset of verbalized spontaneous thought with annotated transcripts and self-report trait measures

The paper provides an fMRI dataset of verbalized spontaneous thought with transcripts, timestamps, and self-report measures for studying neural dynamics and individual differences.

AI-Supported Extraction of Functional Tissue Unit Properties for Human Reference Atlas Construction

The paper introduces an AI framework to automate the extraction of functional tissue unit properties from publications, supporting the construction of the Human Reference Atlas.

OmniExtract: An automatic data extraction tool based on Large Language Model and Prompt Engineering

OmniExtract is a data extraction tool using LLM and prompt engineering that achieves high accuracy in extracting structured information from text and tables.

aiDIVA - Diagnostics of Rare Genetic Diseases Using Large Language Models

aiDIVA uses AI and LLMs to prioritize causal genetic variants in rare diseases, achieving high accuracy in identifying top candidates.

Searching the Druggable Genome using Large Language Models

The paper develops a server enabling large language models to access drug-gene interaction data through natural language queries.

PubMind: Literature-Based Genetic Variant Extraction and Functional Annotation Using Large Language Models

PubMind extracts and annotates genetic variant functionality from biomedical literature using LLMs, creating a database with over 1.3 million variants.

LinkLlama: Enabling Large Language Model for Chemically Reasonable Linker Design

LinkLlama improves linker design by combining natural language prompts with 3D spatial awareness, achieving higher chemical validity than existing methods.

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.

Queryome: Orchestrating Retrieval, Reasoning, and Synthesis across Biomedical Literature

Queryome is a deep research system that improves biomedical literature synthesis through iterative reasoning and retrieval, achieving higher accuracy than existing methods.

GeneInsight: Condensing Gene Set Knowledge via Language Models

GeneInsight synthesizes gene set annotations using topic modeling and language models to provide interpretable summaries that reveal biologically significant insights overlooked by traditional enrichment analyses.

DrugLM: A Unified Framework to Enhance Drug-Target Interaction Predictions by Incorporating Textual Embeddings via Language Models

DrugLM enhances drug-target interaction predictions by integrating textual embeddings from large language models into DTI models, showing strong performance without fine-tuning.

Benchmarking biochemical networks generated by large language models

This study benchmarks large language models' ability to generate biochemical networks, finding moderate accuracy in signaling and metabolic networks but variable prediction performance.

PeptiVerse: A Unified Platform for Therapeutic Peptide Property Prediction

PeptiVerse provides a unified platform for predicting therapeutic peptide properties using both amino acid sequences and SMILES-based representations, supporting early-stage development and generative design.

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.

Agentomics: An Agentic System that Autonomously Develops Novel State-of-the-art Solutions for Biomedical Machine Learning Tasks

Agentomics is an autonomous system that generates ML models for biomedical tasks, outperforming existing agentic systems and human-engineered models on 11/20 datasets.

Predicting the Evolutionary and Functional Landscapes of Viruses with a Unified Nucleotide-Protein Language Model: LucaVirus

LucaVirus predicts viral evolution and function using a unified nucleotide-protein language model, achieving state-of-the-art results in key virology tasks.

Intelligent Tool Orchestration for Rapid Mechanistic Model Prototyping: MCP Servers as AI-Biology Interfaces

This paper introduces an AI-assisted framework for rapid prototyping of multicellular mechanistic models using LLMs and MCP servers to integrate multiple modeling tools.

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.

Embracing naturalistic paradigms: substituting GPT predictions for human judgments

This study evaluates GPT's ability to predict human affective judgments in narratives and finds it correlates with human ratings and neural activity inferences.

MARRVEL-MCP enables natural language variant interpretation through autonomous workflow construction

MARRVEL-MCP enables natural language variant interpretation by allowing LLMs to autonomously construct workflows using curated genomic tools, improving performance over models without tool access.

MetaBeeAI: an AI pipeline for structured evidence extraction from biological literature

MetaBeeAI extracts structured evidence from biological literature using a modular LLM pipeline with expert validation, improving consistency and reproducibility across studies.

From Metabolomics to Function: Ranking Plant Stem Cell Metabolomes for Use in Health and Cosmetics

The paper ranks plant callus metabolomes for health and cosmetic applications by identifying functionally relevant metabolites using a new pipeline.

AutoLead: An LLM-Guided Bayesian Optimization Framework for Multi-Objective Lead Optimization

AutoLead uses LLMs and Bayesian optimization to optimize drug-like molecules for multiple objectives, achieving state-of-the-art results on a new benchmark dataset.

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.

Leveraging large language models for metabolic engineering design

This paper develops a large language model-based method to extract metabolic engineering strategies from research articles and trains a hybrid model to predict engineering targets, outperforming traditional approaches.

TACO: TabPFN Augmented Causal Outcomes for Early Detection of Long COVID

TACO detects presymptomatic Long COVID by combining causal inference with a foundation model, achieving higher precision with fewer features and no hyperparameter tuning.

Negative design enables cell-free expression and folding of designed transmembrane β-Barrels

The paper demonstrates that negative design, which locally destabilizes β-strands, improves folding efficiency of synthetic transmembrane β-barrels in a cell-free system.