large language model

scGNN+: Adapting ChatGPT for Seamless Tutorial and Code Optimization

scGNN+ integrates ChatGPT with graph neural networks to improve code optimization, tutorial generation, and visualization in single-cell research.

Multi-Modal Large Language Model Enables All-Purpose Prediction of Drug Mechanisms and Properties

DrugChat, a multi-modal LLM, predicts drug mechanisms and properties through free-form text, outperforming GPT-4 and specialized models in accuracy and versatility.

Bilingual Language Model for Protein Sequence and Structure

ProstT5, a ProtT5 fine-tuned to translate between amino acids and Foldseek 3Di tokens, predicts structure strings for remote homology search and performs inverse folding.

FragLlama: Next-fragment prediction for molecular design

FragLlama predicts next molecular fragments for drug design, generating molecules with desired properties and exploring novel chemical spaces.

RNA-DCGen: Dual Constrained RNA Sequence Generation with LLM-Attack

RNA-DCGen generates RNA sequences with specific structural or functional properties using a language model, outperforming random baselines in structure prediction.

GexMolGen: Cross-modal Generation of Hit-like Molecules via Large Language Model Encoding of Gene Expression Signatures

GexMolGen generates hit-like molecules using gene expression signatures through a cross-modal approach, producing valid molecules from both in- and out-of-domain transcriptome data.

Large language models overcome the challenges of unstructured text data in ecology

This study evaluates three large language models for automating ecological data extraction from text, finding GPT 4 performs best across tasks with high accuracy.

FoodAtlas: Automated Knowledge Extraction of Food and Chemicals from Literature

The paper develops a pipeline using large language models to extract food-chemical relationships from literature, identifying many novel interactions.

Optimizing biomedical information retrieval with a keyword frequency-driven Prompt Enhancement Strategy

This paper enhances biomedical question-answering using a keyword frequency-driven prompt strategy to improve LLM responses and reduce hallucinations.

Leveraging Generative AI to Accelerate Biocuration of Medical Actions for Rare Disease

AutoMAxO uses LLMs to semi-automate MAxO curation for rare diseases, identifying 538 confirmed disease-treatment annotations from 4,918 medical abstracts.

Multi-Modal Large Language Model Enables Protein Function Prediction

ProteinChat is a multi-modal large language model that predicts protein functions from amino acid sequences, outperforming general-purpose LLMs and task-specific models.

Bioinformatics Copilot 2.0 for Transcriptomic Data Analysis

Bioinformatics Copilot 2.0 enhances single-cell transcriptomic analysis with internal server processing, user control, real-time information access, and result documentation.

DepoScope: accurate phage depolymerase annotation and domain delineation using large language models

DepoScope uses large language models to accurately annotate phage depolymerases and their domains, improving detection through sequence and amino-acid level predictions.

Assessing the ability of ChatGPT to extract natural product bioactivity and biosynthesis data from publications

This study evaluates ChatGPT's ability to extract natural product bioactivity and biosynthesis data from scientific publications, finding it effective for identifying natural product discovery and bioactivity but less accurate for extracting accession numbers.

Video Foundation Models for Animal Behavior Analysis

This paper evaluates frozen video foundation models for animal behavior analysis, showing they achieve competitive performance across diverse datasets and tasks with minimal adaptation.

Genetic Transformer: An Innovative Large Language Model Driven Approach for Rapid and Accurate Identification of Causative Variants in Rare Genetic Diseases

This paper introduces Genetic Transformer (GeneT), an LLM-driven method that significantly reduces candidate variant numbers and improves recall in rare disease diagnosis.

Genomics-FM: Universal Foundation Model for Versatile andData-Efficient Functional Genomic Analysis

Genomics-FM is a foundation model that reduces reliance on labeled data and outperforms existing models in genome annotation, epigenomic prediction, and variant effect assessment.

A Foundational Large Language Model for Edible Plant Genomes

This paper introduces a large language model for plant genomics that provides state-of-the-art predictions for regulatory annotations and variant prioritization, along with a benchmark dataset for deep learning methods.

scKEPLM: Knowledge enhanced large-scale pre-trained language model for single-cell transcriptomics

scKEPLM integrates a biology knowledge graph into single-cell transcriptome pre-training to improve gene and cell representation, achieving state-of-the-art performance in multiple downstream tasks.

Quantifying the massive pleiotropy of microRNA: a human microRNA-disease causal association database generated with ChatGPT

The paper introduces miRAIDD, a database of human miRNA-disease causal associations generated using large language models, and quantifies miRNA pleiotropy and its biological characteristics.

CodonBERT: Large Language Models for mRNA Design and Optimization

CodonBERT is a large language model for mRNA design and optimization that outperforms previous methods on a flu vaccine dataset.

Large language models are universal biomedical simulators

This paper introduces SimulateGPT, a text-based simulator using large language models to predict biomedical outcomes without domain knowledge or manual tuning.

Analysis of live cell data with G-DNABERT supports a role for G-quadruplexes in chromatin looping

The paper uses G4-DNABERT to map G-quadruplexes to enhancer and promoter regions, supporting their role in chromatin looping and gene regulation.

Using GPT-4 to write a scientific review article: a pilot evaluation study

This study evaluates GPT-4's ability to generate text, tables, and diagrams for biomedical review papers and suggests improvements for scientific use.

Harnessing generative AI to annotate the severity of all phenotypic abnormalities within the Human Phenotype Ontology

This paper uses GPT-4 to automatically annotate the severity of phenotypic abnormalities in the Human Phenotype Ontology, achieving high accuracy in clinical characteristic identification.

Large language models identify causal genes in complex trait GWAS

This study shows large language models can accurately prioritize causal genes at GWAS loci, outperforming existing methods and offering a scalable solution.

BioInformatics Agent (BIA): Unleashing the Power of Large Language Models to Reshape Bioinformatics Workflow

This paper introduces BIA, an intelligent agent using LLMs to automate bioinformatics tasks, demonstrating its effectiveness with scRNA-seq data and outlining strategies for improvement.

A generative foundation model for antibody sequence understanding

FAbCon is a generative antibody-specific language model that improves antigen binding prediction and antibody design with increasing model size.

A Data-Intelligence-Intensive Bioinformatics Copilot System for Large-scale Omics Researches and Scientific Insights

This paper introduces a bioinformatics copilot system that combines AI and human expertise to enhance large-scale omics research and uncover biological insights.

CellAgent: LLM-Driven Multi-Agent Framework for Natural Language-Based Single-Cell Analysis

CellAgent uses an LLM-driven multi-agent framework to automate scRNA-seq and spatial transcriptomics analysis through natural language interactions, achieving 60% efficiency improvement over manual methods.