Biomedical Text Mining and Ontologies

Vocabulary Matters: An Annotation Pipeline and Two Deep Learning Algorithms for Enzyme Named Entity Recognition

This paper develops an annotation pipeline and two deep learning models for enzyme named entity recognition, achieving high performance with SciBERT-BiLSTM outperforming BioBERT-BiLSTM.

Large Language Models for Pathway Curation: A Preliminary Investigation

This paper investigates using Large Language Models for pathway curation, showing improved performance over existing methods with a GPT-3.5 fine-tuned model.

Large Language Models to process, analyze, and synthesize biomedical texts - a scoping review

This scoping review summarizes the use of large language models in biomedical NLP tasks, highlighting their growing application in text classification, relation extraction, and named entity recognition.

Text mining and portal development for gene-specific publications on Alzheimer's disease and other neurodegenerative diseases

The paper develops a web portal and NLP pipeline to extract and visualize gene-specific Alzheimer's disease publications from PubMed.

Bringing PanglaoDB to 5-star Linked Open Data using Wikidata

The paper maps PanglaoDB cell-type markers to Wikidata to enhance reusability and FAIRness, creating structured associations and demonstrating new biological insights.

Ontology-based modeling, integration, and analysis of heterogeneous clinical, pathological, and molecular kidney data for precision medicine

The paper uses ontologies to integrate clinical, pathological, and molecular kidney data for precision medicine analysis.

Harnessing the Power of Large Language Models (LLMs) to Unravel the Influence of Genes and Medication on Biological Processes of Wound Healing

This study evaluates GPT-3.5 and GPT-4 for identifying biological processes and regulatory relationships in wound healing texts, finding GPT-4 performs better without fine-tuning.

GO2Sum: Generating Human Readable Functional Summary of Proteins from GO Terms

GO2Sum generates human-readable protein function summaries from GO terms using a fine-tuned T5 model, outperforming the base T5 model in creating function descriptions for UniProt entries.

Human-in-the-loop approach to identify functionally important residues of proteins from literature

The paper presents a human-in-the-loop system for identifying residue-level functional annotations from literature, achieving high precision, recall, and F1-measure.

Curating genomic disease-gene relationships with Gene2Phenotype

The paper describes a structured curation process for the Gene2Phenotype project to systematically evaluate and organize genomic disease-gene relationships.

preon: Fast and accurate entity normalization for drug names and cancer types in precision oncology

preon normalizes drug names and cancer types in large-scale data for precision oncology.

Fine-tuning BERT models to extract transcriptional regulatory interactions of bacteria from biomedical literature

The paper fine-tunes BERT models to extract transcriptional regulatory interactions from bacterial literature, achieving high performance and reconstructing a significant portion of a bacterial transcriptional regulatory network.

Positioning Genomic Features in Biomedical Knowledge Graphs using the Homo sapiens Chromosomal Location Ontology for GRCh38 (HSCLO38)

The paper develops HSCLO38, a framework for connecting genomic features in a knowledge graph, and evaluates its performance with large-scale genomic data.

PuMA: PubMed Gene-Celltype-Relation Atlas

PuMA extracts and visualizes gene-celltype relations from PubMed to aid in celltype annotation.

Phenomics Assistant: An Interface for LLM-based Biomedical Knowledge Graph Exploration

Phenomics Assistant is a chat-based interface that uses LLMs to query the Monarch knowledge graph, improving reliability for non-expert users to explore disease-gene-phenotype relationships.

reguloGPT: Harnessing GPT for Knowledge Graph Construction of Molecular Regulatory Pathways

reguloGPT constructs a knowledge graph of molecular regulatory pathways using GPT-4 for entity recognition and relationship extraction, improving over existing methods and demonstrating utility in cancer research.

A Comprehensive Evaluation of Large Language Models in Mining Gene Interactions and Pathway Knowledge

This paper evaluates 21 large language models for mining gene interactions and pathway knowledge, finding significant performance differences between API-based and open-source models.

CoNECo: A Corpus for Named Entity recognition and normalization of protein Complexes

The paper introduces CoNECo, a corpus for NER and NEN of protein complexes, achieving F1-scores of 73.7% and 61.2% with transformer-based and dictionary-based taggers, respectively.

Mining drug-target interactions from biomedical literature using chemical and gene descriptions-based ensemble transformer model.

The paper develops an ensemble transformer model using gene and chemical descriptions to extract drug-target interactions from biomedical literature, achieving high performance on a benchmark dataset.

Pannotator integrated with Medpipe provides immunological and subcellular location features using a microservice

The paper integrates the Medpipe microservice with Pannotator to enhance genomic annotation with immunological and subcellular location features.

RegulaTome: a corpus of typed, directed, and signed relations between biomedical entities in the scientific literature

RegulaTome provides a comprehensive corpus of biomedical relations, enabling improved relation extraction with a Transformer model achieving 66.6% F1-score.

Dark kinase annotation, mining and visualization using the Protein Kinase Ontology

The paper extends the Protein Kinase Ontology (ProKinO) to uncover new insights about dark kinases, identifying PAK5 as a frequently mutated kinase in cancers and highlighting its potential in drug discovery.

simona: a comprehensive R package for semantic similarity analysis on bio-ontologies

The paper introduces simona, an R package for semantic similarity analysis on bio-ontologies, benchmarking methods and showing topology-based approaches are effective for less-studied organisms.

Semantic Representation of Neural Circuit Knowledge in Caenorhabditis elegans

The paper develops CeN-CAM, a semantic model based on GO-CAM, to represent causal relationships in C. elegans neural circuits and behaviors using literature data.

BioWiC: An Evaluation Benchmark for Biomedical Concept Representation

The paper introduces BioWiC, a benchmark to evaluate how well language models represent biomedical concepts in context, and establishes baselines using various large language models.

Serial KinderMiner (SKiM) Discovers and Annotates Biomedical Knowledge Using Co-Occurrence and Transformer Models

SKiM discovers and annotates biomedical knowledge using co-occurrence and transformer models to find A-B-C relationships between user-defined concepts.

Europe PMC Annotated Full-text Corpus for Gene/Proteins, Diseases and Organisms

The paper introduces a human-annotated full-text corpus for biomedical entities, including genes, proteins, diseases, and organisms, to support deep learning approaches in NLP.

PhenoID, a language model normalizer of physical examinations from genetics clinical notes

PhenoID is a transformer-based NLP system that normalizes dysmorphology exam findings into HPO terms, achieving state-of-the-art performance on phenotype extraction from clinical notes.

Clustering rare diseases within an ontology-enriched knowledge graph

The paper clusters rare diseases using a knowledge graph with node embeddings, identifying 37 clusters with shared genes and semantic similarities.

ENQUIRE RECONSTRUCTS AND EXPANDS CONTEXT-SPECIFIC CO-OCCURRENCE NETWORKS FROM BIOMEDICAL LITERATURE

ENQUIRE reconstructs and expands context-specific co-occurrence networks from biomedical literature to identify gene interactions and pathways relevant to specific topics.