large language model

Biological Database Mining for LLM-Driven Alzheimer's Disease Drug Repurposing

This paper develops an LLM-driven pipeline for Alzheimer's drug repurposing by integrating ontologies and drug databases, showing improved alignment with expert knowledge and reduced hallucinations.

Protein2Text: Providing Rich Descriptions for Protein Sequences

The paper introduces BetaDescribe, a model that generates rich textual descriptions of proteins, enhancing understanding of their functions and properties.

NJGPT: A Large Language Model-Driven, User-Friendly Solution for Phylogenetic Tree Construction

NJGPT uses a large language model to construct phylogenetic trees from sequence data, offering a user-friendly interface but with limitations in handling large datasets and speed.

SpaCCC: Large language model-based cell-cell communication inference for spatially resolved transcriptomic data

SpaCCC uses a fine-tuned LLM and gene network to infer cell-cell communications from spatial transcriptomic data, outperforming existing methods and revealing hidden ligand-receptor pairs.

Tackling the Complexity of Spatial Transcriptomics Data Interpretation with Large Language Models

This study uses large language models to analyze spatial transcriptomics data from a murine melanoma model, identifying immunosuppressive mechanisms in the tumor microenvironment.

VaxLLM: Leveraging Fine-tuned Large Language Model for automated annotation of Brucella Vaccines

VaxLLM automates Brucella vaccine article classification and annotation with high accuracy, outperforming non-fine-tuned models and enabling structured data integration.

The effectiveness of Large Language Models with RAG for auto-annotating phenotype descriptions

This study uses Large Language Models with Retrieval-Augmented Generation to auto-annotate plant phenotypes with ontology terms, achieving results comparable to expert annotations.

Network for Knowledge Organization (NEKO): an AI knowledge mining workflow for synthetic biology research

NEKO is an AI workflow using LLMs to extract and organize scientific knowledge from literature, providing more specific and actionable insights than existing models.

Rapid protein evolution by few-shot learning with a protein language model

This paper introduces EVOLVEpro, a few-shot learning framework using protein language models to rapidly improve protein activity with minimal experimental data.

Discovering CRISPR-Cas system with self-processing pre-crRNA capability by foundation models

The paper introduces CHOOSER, a foundation model-based framework for discovering CRISPR-Cas systems with self-processing pre-crRNA capability, identifying 11 novel homologs and validating one for functional use.

Multi-purpose controllable protein generation via prompted language models

This paper introduces PROPEND, a protein design method using prompted language models to directly control multiple properties, achieving higher functional recovery than traditional pipelines.

A language model assistant for biocatalysis

This paper introduces LM-ABC, a language model assistant for biocatalysis that streamlines enzyme engineering workflows using natural language inputs and integrates with computational tools.

FAPM: Functional Annotation of Proteins using Multi-Modal Models Beyond Structural Modeling

FAPM improves protein functional annotation by combining protein sequence and language models, outperforming sequence-only methods on benchmarks and phage proteins with few homologs.

The Virtual Lab: AI Agents Design New SARS-CoV-2 Nanobodies with Experimental Validation

Role-specialised LLM agents under an LLM principal investigator, with light human steering, assembled an ESM/AlphaFold-Multimer/Rosetta pipeline and designed 92 SARS-CoV-2 nanobodies; two bound recent variants better.

De novo design of triosephosphate isomerases using generative language models

This study uses generative language models to design de novo triosephosphate isomerases, which show catalytic activity in E. coli.

Language model generates cis-regulatory elements across prokaryotes

The paper uses a language model to design cis-regulatory elements for prokaryotes without experimental data, achieving high success rates in gene expression and promoter activity.

DGRNA: a long-context RNA foundation model with bidirectional attention Mamba2

DGRNA is a long-context RNA foundation model using bidirectional Mamba2 that outperforms existing RNA language models on six downstream tasks.

TaxonGPT: Taxonomic Classification Using Generative Artificial Intelligence

TaxonGPT uses generative AI to create taxonomic descriptions and keys, with error-checking to improve accuracy.

Automated Harmonization and Large-Scale Integration of Heterogeneous Biomedical Sample Metadata Using Large Language Models

This paper introduces a framework using large language models to automate metadata harmonization and integration across biomedical studies, achieving higher recovery rates than traditional methods.

Integration of protein and coding sequences enables mutual augmentation of the language model

This paper introduces a dual-language foundation model that integrates protein and coding sequences, showing improved performance in discriminative tasks and generating coding sequences with higher protein yield.

Semantic search using protein large language models detects class II microcins in bacterial genomes

The paper uses protein large language models to detect class II microcins in bacterial genomes, finding embeddings outperform sequence-based methods like BLAST in identifying known and novel microcins.

Harnessing GPT-4 for Automated Curation of E3-Substrate Relationships in the Ubiquitin-Proteasome System

This paper uses GPT-4 to automate curation of E3-substrate relationships from literature, resulting in a database of 3,294 pairs with high accuracy.

Multimodal learning of transcriptomes and text enables interactive single-cell RNA-seq data exploration with natural-language chats

CellWhisperer enables chat-based exploration of single-cell RNA-seq data using natural language by connecting transcriptomes with text through multimodal learning.

A Suite of Foundation Models Captures the Contextual Interplay Between Codons

The paper introduces cdsFM, a suite of codon-resolution large language models that predict the functional consequences of synonymous codon choices and identify pathogenic variants.

GeneCompass: Deciphering Universal Gene Regulatory Mechanisms with Knowledge-Informed Cross-Species Foundation Model

GeneCompass is a cross-species foundation model pre-trained on single-cell transcriptomes from human and mouse, integrating biological knowledge to improve gene regulatory mechanism understanding and downstream tasks.

An AI Agent for Fully Automated Multi-omic Analyses

AutoBA is an AI agent that automates multi-omic analyses using large language models, offering adaptability and security across various sequencing and omics data types.

Leveraging genomic large language models to enhance causal genotype-brain-clinical pathways in Alzheimer's disease

This study introduces epiBrainLLM, a framework using genomic LLMs to improve causal analysis of genotype-brain-clinical pathways in Alzheimer's disease.

IgBlend: Unifying 3D Structures and Sequences in Antibody Language Models

IgBlend integrates 3D structures and sequences for antibody language models, outperforming existing models in benchmarks and showing correlation with binding affinities.

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.