Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

The microarray technology permits simultaneous monitoring of several thousand gene expressions for each sample. However, classifying such samples into their distinct classes may be quite challenging owing to the higher number of genes (the features) over the actual samples. As a consequence, there is a need to make an investigation of the new and the robust techniques of machine learning that can accurately classify the microarray data. The B cells further give some humoral immunity by means of distinguishing into the Plasma Cells (PCs) that are antibody-secreting which is a process that needs a cellular division linked to the DeoxyRibonucleic Acid (DNA) hypomethylation. Classification of cancer is another critical issue in the analysis of cancer treatment. A very effective method in the classification of cancer is its gene selection. But choosing the gene subset that increases the accuracy may be a Non-Deterministic Polynomial (NP)-hard problem. For this work, a methodology was proposed for choosing an ideal set of genes which permit classification of the class of disease based on gene expression along with a good accuracy. The proposed method was compared with feature selection method that employed the Genetic Algorithm (GA).The feature selection of the proposed work is using the Artificial Bee Colony (ABC) algorithm with feature selection that is Levy search based. The results have proved that this new technique had an increase in accuracy of classification which was compared to the Genetic Algorithm and also the Artificial Bee Colony based technique. This also had a very fast convergence. © 2020, World Academy of Research in Science and Engineering. All rights reserved.

Department of Database Systems

School of Computer Science and Engineering

Vellore Campus

World University root node

International Journal of Advanced Trends in Computer Science and Engineering

Hybrid artificial bee colony based feature selection in bone marrow plasma cell gene expression data

The Microarray technology permits simultaneous monitoring of several gene expressions for every sample. But unfortunately, this classification of such samples into the distinct classes has often been found to difficult as the actual number of genes (the features) will to a great extent exceed the actual number of the samples. There is a high dimensionality in gene and its expression data which is a huge challenge in many of the problems of classification. Cloud computing is that popular concept of computing which performs a processing of a data of huge volumes by making use of highly accessible resources that are geographically distributed and accessed by the users based on the policy of pay as per use. In spite of the several steps in that of the B Cell that are now elucidate the last few stages of that of the plasma cell (PC) based differentiation that have not been understood yet. The PCs had generated at the time of primary and humoral immune responses that have started their differentiation within their light zones for the germinal centers of such light zones of that of the lymph nodes or the ones in the red pulp of the spleen having a life span of about few days. The selection of the features will aim at the maintenance of their original features of such data and at this time it will seek at identifying their main features and will weed out all those that are irrelevant for building of a learning model that is impactful. Identifying one global maximum will be Non-deterministic Polynomial (NP)-hard and if the criterion is decomposable or possesses the properties that can make some approximate type of optimization easy. The artificial bee colony (ABC) is that one that is used widely and applied for solving all real world problems. The stochastic diffusion search (SDS) will be an efficient multi-agent global search based technique of optimization applied to that of several problems and here the hybrid ABC with that of the SDS feature selection will be proposed and these images will be grouped as either normal or abnormal PC of the bone marrow that is based on this gene expression data. The support vector machine is that algorithms of supervised learning that is capable of being able to solve the complex problems in classification. This proposed method of gene selection will yield a comparable performance for the classification on being compared to that of the currently existing classifiers providing another new insight in case of feature selection. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Cluster Computing

A wrapper based feature selection in bone marrow plasma cell gene expression data

There are different types of fatal diseases that could possibly outspread to various parts of the body. It thus becomes obligatory to predict the existence of such anomalies, in order to prune the extent of their spread. Examining the characteristics of genes provides a deep intuition about the disease classification, as they play a vital role in influencing how an organism appears, behaves and survives in an environment. The detection of the abnormal genes could be efficiently modelled using statistical methods and machine learning approaches. Gene expression data derived from a microarray could act as an aid for this statistical computation. Microarray being a recent leap in molecular biology, provides a scope for hybridization of DNA samples that can be interpreted as values based on the gene expression level that the genome possesses. We propose an idea to select a subset of features from the huge number of samples retrieved from the gene expression profiles using Boruta feature selection algorithm. A comparative study with various supervised classification algorithms is made to categorize this subset to a normal and deviant gene. This serves to discover the most appropriate algorithm to classify the gene expression data. Hence assorting the abnormal genes in future could be accelerated with ease. © BEIESP.