Computational Molecular Biology 2016, Vol.6, No.3, 1-6
1
Research Report Open Access
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
Corresponding author Email
Computational Molecular Biology, 2016, Vol.6, No.3 doi
Received: 25 Feb., 2016
Accepted: 21 Jul., 2016
Published: 22 Nov., 2016
Copyright © 2016
Dubey, 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 (doi
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
1 Introduction
Machine learning is a branch of artificial intelligence, concerned with the design and development of algorithms
that allow computers to evolve behaviours based on empirical data, such as from sensor data or databases. A
learner can take advantage of examples (data) to capture characteristics of interest of their unknown underlying
probability distribution. Data can be seen as examples that illustrate relations between observed variables. A major
focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent
decisions based on data; the difficulty lies in the fact that the set of all possible behaviours given all possible
inputs is too large to be covered by the set of observed examples (training data). Hence the learner must generalize
from the given examples, so as to be able to produce a useful output in new cases. Machine learning requires
cross-disciplinary proficiency in several areas, such as probability theory, statistics, pattern recognition, cognitive
science, data mining, adaptive control, computational neuroscience and theoretical computer science for analyzing
data.
2 Machine Learning Techniques and its Applications
Machine learning techniques have been widely applied to biological sequences, e.g. to predict drug resistance in
HIV-1 from sequences of drug target proteins and protein functional classes. As deletions and insertions are
frequent in biological sequences, a major limitation of current methods is the inability to handle varying sequence
lengths. Machine learning is a promising alternative to existing methods, especially for protein sequences of
variable length. Hence many scientists or biological workers try machine learning methods to analyse biological
data and often generating useful patterns.
Starting from 1992 or earlier scientists thought about protein folding problem which still an unsolved puzzle.
According to Chou (1994 and 2001) and Wu (2002), a protein is characterized as vector of 20-D space, in which
its 20 components are defined by the composition of its 20 amino acid and the similarity of two proteins is
proportional to the mutual projection of their characterized vectors and hence inversely proportional to the size of
their correlation angles.
