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Applications of Machine Learning: Cutting Edge Technology in HIV Diagnosis, Treatment and Further Research | Dubey | Computational Molecular Biology

Research Report

Applications of Machine Learning: Cutting Edge Technology in HIV Diagnosis, Treatment and Further Research  

Anubha Dubey
Independent researcher and analyst Bioinformatics, Near Brethren Assembly Gayatri Nagar, Katni, 483501, Madhya Pradesh, India
Author    Correspondence author
Computational Molecular Biology, 2016, Vol. 6, No. 3   doi: 10.5376/cmb.2016.06.0003
Received: 25 Feb., 2016    Accepted: 21 Jul., 2016    Published: 01 Dec., 2016
© 2016 BioPublisher Publishing Platform
This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article:

Dubey A., 2016, Applications of machine learning: cutting edge technology in HIV diagnosis, treatment and further research, Computational Molecular Biology, 6(3): 1-6

Abstract

In the last few years there is a remarkable progress of research in machine learning. This field has gained an unprecedented popularity, several new areas have developed and some are gaining new momentum. Machine learning is useful in cases where algorithmic solutions are not available i.e. there is lack of formal models or the knowledge about the application domain is poorly defined. The fact that various scientific communities are involved in machine learning research led this scientific field to incorporate ideas from different areas, such as computational learning theory, artificial neural networks, statistics, stochastic modelling, genetic algorithms and pattern recognition. The domain of machine learning has gained immense popularity in HIV diagnosis, screening, treatment and nowadays for designing & production of vaccines for cure of HIV. In this review article it is summarized the progression of machine learning techniques in HIV-AIDS.

Keywords
Modelling; Symbolic; Neural network; Genetic algorithms
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