A comprehensive machine learning for high throughput Tuberculosis sequence analysis, functional annotation, and visualization

A comprehensive machine learning for high throughput Tuberculosis sequence analysis, functional annotation, and visualization

Abstract With human guidance, computers now use machine learning (ML) in artificial intelligence (AI) to learn from data, detect trends, and make predictions. Software can adapt and improve with new information. Imaging scans leverage pattern recognition to predict outcomes, diagnose disorders, and...

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Main Authors: Md. Saddam Hossain, Md. Parvez Khandocar, Farzana Akter Riti, Md. Yeakub Ali, Prithbey Raj Dey, S M Jahurul Haque, Amira Metouekel, Atrsaw Asrat Mengistie, Mohammed Bourhia, Farid Khallouki, Khalid S. Almaary
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
ML
Bioinformatics
TB
DEGs
Gene ontology
PPIs
Online Access:https://doi.org/10.1038/s41598-025-98654-0
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https://doi.org/10.1038/s41598-025-98654-0

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