Cargando…
Evaluation of machine learning classifiers for predicting essential genes in Mycobacterium tuberculosis strains
Accurate investigation and prediction of essential genes from bacterial genome is very important as it might be explored in effective targets for antimicrobial drugs and understanding biological mechanism of a cell. A subset of key features data obtained from 14 genome sequence-based features of 20...
Autores principales: | Mukul Das, Monish, Sarkar, Keka |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Biomedical Informatics
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10492903/ https://www.ncbi.nlm.nih.gov/pubmed/37701504 http://dx.doi.org/10.6026/973206300181126 |
Ejemplares similares
-
An integrated machine learning approach for predicting DosR-regulated genes in Mycobacterium tuberculosis
por: Zhang, Yi, et al.
Publicado: (2010) -
Essential gene prediction using limited gene essentiality information–An integrative semi-supervised machine learning strategy
por: Nandi, Sutanu, et al.
Publicado: (2020) -
Machine Learning Algorithms Evaluate Immune Response to Novel Mycobacterium tuberculosis Antigens for Diagnosis of Tuberculosis
por: Meier, Noëmi Rebecca, et al.
Publicado: (2021) -
Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data
por: Yang, Yang, et al.
Publicado: (2018) -
Evaluation of the effect of Pulicaria gnaphalodes and Perovskia abrotanoides essential oil extracts against Mycobacterium tuberculosis strains
por: Hozoorbakhsh, Fereshte, et al.
Publicado: (2016)