The development of an ensemble machine learning pipeline for predicting resistance gene functions using the Comprehensive Antibiotic Resistance Database (CARD) demonstrates superior performance while maintaining computational efficiency, making it suitable for deployment in resourceconstrained environments.
Princewill Ahumaraeze, Ofonime Dominic Okon, P. Asuquo et al.· E3S Web of Conferences· 0 citations
This study introduces an alignment-free computational framework for identifying microbial bioremediation potential, establishing a standardized, multi-domain dataset encompassing Bacteria, Protists, Archaea, and Fungi, and integrating metadata on polymer interactions to fill existing data gaps.
U. Luke, Emem Okon Abang, Etimbuk Daniel Akpan et al.· E3S Web of Conferences· 0 citations
This approach combines semantic understanding of clinical narratives with structural modeling of patient-disease-treatment relationships and successfully validates synthetic EHR data utility for privacy-preserving healthcare AI development while addressing critical requirements necessary for clinical decision support s...
U. Luke, P. Asuquo, Victor Anaga et al.· E3S Web of Conferences· 0 citations
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