Title:Machine Learning and Tubercular Drug Target Recognition
VOLUME: 20 ISSUE: 27
Author(s):Li M. Fu
Affiliation:Research and Development, Veterans Affairs Healthcare System, 5901 E. 7th Street, Long Beach, CA 90822, USA.
Keywords:Tuberculosis, persistence, machine learning, microarray, drug target.
Abstract:Tuberculosis (TB) remains to be a global major public-health threat, causing millions of deaths each year. A major difficulty in
dealing with TB is that the causative bacterium, Mycobacterium tuberculosis, can persist in host tissue for a long period of time even after
treatment. Mycobacterial persistence has become a central research focus for developing next-generation TB drugs. Latest genomic
technology has enabled a high-throughput approach for identifying potential TB drug targets. Each gene product can be screened for its
uniqueness to the TB metabolism, host-pathogen discrimination, essentiality for survival, and potential for chemical binding, among
other properties. However, the exhaustive search for useful drug targets over the entire genome would not be productive as expected in
practice. On the other hand, the problem can be formulated as pattern recognition or inductive learning and tackled with rule-based or statistically
based learning algorithms. Here we review the perspective that combines machine learning and genomics for drug discovery in
tuberculosis.
