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Updated: May 24, 2025

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An NLP-based method to mine gene and function relationships from published articles.

Nilesh Kumar1, M Shahid Mukhtar2,3

  • 1Department of Biology, University of Alabama at Birmingham, 3100 East Science Hall, 902 14th Street South, Birmingham, AL, 35294, USA.

Scientific Reports
|March 3, 2025
PubMed
Summary
This summary is machine-generated.

We developed PATHAK, a natural language processing tool, to identify gene functions from scientific papers. This method accurately links genes to functions, aiding biological research.

Keywords:
Gene functionGene ontologyNLPPublished articles

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Understanding gene function is crucial for biological and medical advancements.
  • The complexity of biological systems and research data presents challenges in identifying gene-function relationships.
  • Existing methods may not efficiently mine this information from vast scientific literature.

Purpose of the Study:

  • To introduce PATHAK, a novel natural language processing (NLP)-based method for extracting gene-function relationships from scientific articles.
  • To demonstrate the adaptability and applicability of PATHAK across diverse scientific domains.
  • To accelerate the discovery of gene functions and their roles in biological processes.

Main Methods:

  • PATHAK utilizes a pre-trained Transformer language model to generate sentence embeddings from scientific documents.
  • It identifies associations between genes and functional annotations by comparing sentence and gene ontology (GO) term embeddings.
  • The method was applied to over 17,000 research articles on *Arabidopsis thaliana*.

Main Results:

  • PATHAK assigned approximately 1493 Gene Ontology (GO) terms to 10,976 genes.
  • The model achieved moderate-to-high predictive accuracy, with a 57% overlap in GO terms compared to known annotations on TAIR.
  • This included 1271 exact matches and 4826 partially related terms, highlighting its effectiveness.

Conclusions:

  • PATHAK significantly advances the understanding of gene functionality by efficiently mining published research.
  • The method has the potential to accelerate discoveries in plant development, growth, and stress responses, as well as other biological systems.
  • This NLP-based approach offers a scalable solution for functional genomics research.