Exciting News: Joining the Natural Language Processing for Biocuration Working Group at AgBioData

I am thrilled to announce that I have joined the Natural Language Processing (NLP) for Biocuration Working Group at AgBioData! This initiative brings together experts in biocuration, artificial intelligence, and genomics to enhance the integration of NLP tools into agricultural and biological databases.

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Why NLP for Biocuration?

Biocuration is a critical yet time-consuming process in ensuring FAIR (Findable, Accessible, Interoperable, and Reusable) data. Given the rapid increase in biological data, NLP models can significantly aid curators by automating data extraction, organization, interpretation, and validation. However, most existing NLP applications have been tailored for human or model organism datasets, presenting unique challenges when applied to plant and livestock data.

Objectives of the Working Group

As a part of this working group, I will collaborate with experts to:

• Define key use cases for NLP in biocuration within AgBioData databases.

• Identify common entities curated across these databases to facilitate NLP-driven curation.

• Evaluate existing NLP models, tools, and training datasets to understand their limitations in AgBioData.

• Propose strategies and future directions to advance NLP for biocuration.

• Connecting NLP, Biocuration, and Forest Genomic Prediction

My participation in this working group aligns with my training in artificial intelligence for genomic prediction in forest breeding. By improving the curation and accessibility of high-quality biological data, NLP can enhance the development of predictive models for genomic selection, ultimately supporting data-driven strategies in forest genetic improvement. Efficient NLP-based biocuration can facilitate better integration of multi-omics data, phenotype-genotype associations, and genomic selection pipelines, all of which are key components of AI-driven breeding programs.

The Importance of This Initiative

This initiative aligns perfectly with my background in digital competencies and artificial intelligence, particularly in the context of genomic data curation. By leveraging machine learning and NLP, we aim to improve the efficiency and accuracy of data integration and knowledge extraction in agricultural genomics.

I am excited to contribute to this effort and collaborate with leading researchers in the field. Stay tuned for updates as we work toward making biological data more accessible and actionable through NLP!