Abstract
Biomedical literature contains an extensive wealth of information on gene and protein function across various biological processes and diseases. However, navigating this vast and often restricted-access data can be challenging, making it difficult to extract specific insights efficiently. In this study, we introduce a high-throughput pipeline that leverages OpenAI's Generative Pre-Trained Transformer Model (GPT) to automate the extraction and analysis of gene function information. We applied this approach to 84,427 publications on Saccharomyces cerevisiae and 6,452 publications on Schizosaccharomyces pombe, identifying 3,432,749 relationships for budding yeast and 421,198 relationships for S. pombe. This resulted in a comprehensive, searchable online Knowledge Graph database, available at yeast.connectome.tools and spombe.connectome.tools, which offers users extensive access to various interactions and pathways. Our analysis underscores the power of integrating artificial intelligence with bioinformatics, as demonstrated through key insights into important nodes like Hsp104 and Atg8 proteins. This work not only facilitates efficient data extraction in yeast research but also presents a scalable model for similar studies in other biological systems.
Original language | English |
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Article number | 169072 |
Journal | Journal of Molecular Biology |
Volume | 437 |
Issue number | 10 |
DOIs | |
Publication status | Published - May 15 2025 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2025 The Author(s)
ASJC Scopus Subject Areas
- Biophysics
- Structural Biology
- Molecular Biology
Keywords
- bioinformatics
- knowledge graph
- Saccharomyces
- Schizosaccharomyces
- yeast
Press/Media
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New Information Technology Findings from Nanyang Technological University Reported (Yeast Knowledge Graphs Database for Exploring Saccharomyces Cerevisiae and Schizosaccharomyces Pombe)
5/16/25
1 item of Media coverage
Press/Media: Research