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GeNLP: a web tool for NLP-based exploration and prediction of microbial gene function.

Danielle Miller1, Ofir Arias1, David Burstein1

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GeNLP is a web application for exploring microbial gene semantics and predicting gene families using genomic context. It leverages a pre-trained language model for uncovering gene relationships through an interactive interface.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Understanding microbial gene function is crucial for various biological applications.
  • Predicting the function of uncharacterized genes remains a significant challenge in genomics.

Purpose of the Study:

  • To introduce GeNLP, a web application for exploring microbial gene semantics.
  • To enable predictions of uncharacterized gene families using genomic context.
  • To provide an interactive platform for accessing and utilizing gene relationship data.

Main Methods:

  • Utilizes a pre-trained language model for analyzing genomic data.
  • Employs natural language processing (NLP) techniques to uncover gene relationships.
  • Provides a web-based interactive interface for user exploration and prediction.

Main Results:

  • GeNLP facilitates the exploration of microbial gene "semantics".
  • The application enables predictions of gene families based on genomic context.
  • Users can interact with the data to uncover gene relationships.

Conclusions:

  • GeNLP offers a novel approach to understanding microbial gene function.
  • The web application democratizes access to advanced genomic analysis tools.
  • GeNLP empowers researchers to make data-driven predictions about uncharacterized genes.