BactInt: A domain driven transfer learning approach for extracting inter-bacterial associations from biomedical text

CONCLUSION: This study attempts to demonstrate the applicability of transfer learning in a niche field of life sciences where understanding of inter bacterial relationships is crucial to obtain meaningful insights in comprehending microbial community structures across different ecosystems. The study further discusses how such a model can be further improved by fine tuning using limited training data. The results presented and the datasets made available are expected to be a valuable addition in the field of medical informatics and bioinformatics.PMID:38198963 | DOI:10.1016/j.compbiolchem.2023.108012

Source: Computational Biology and Chemistry - January 10, 2024 Category: Bioinformatics Authors: Krishanu Das Baksi Vatsala Pokhrel Anand Eruvessi Pudavar Sharmila S Mande Bhusan K Kuntal Source Type: research