Gene Expression Data Classification and Pattern Analysis Using Data Driven Approach

Gene classification and pattern extraction from gene sequence data is essential in understanding different gene sequence features. The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at complementing micro-array analysis wi...

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Bibliographic Details
Published in:	2021 International Conference on Machine Learning and Cybernetics (ICMLC) pp. 1 - 9
Main Authors:	Ramisa, Aiman Jabeen, Hossain, Ananna, Islam, Sk Md Injamul, Swadesh, Ponuel Mollah, Islam, Md. Toushif, Rahman, Md Anisur, Parvez, Mohammad Zavid
Format:	Conference Proceeding
Language:	English
Published:	IEEE 04-12-2021
Subjects:	Accuracy Classification Data mining Decision trees Feature extraction Gene expression Machine learning Pattern analysis Proteins
Online Access:	Get full text
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Summary:	Gene classification and pattern extraction from gene sequence data is essential in understanding different gene sequence features. The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at complementing micro-array analysis with data and knowl-edge from diverse available sources. Since then, it has been used for various science fields including drug discovery, identification of protein coded genes and phenotype prediction based on gene ex-pression. This paper presents an application of gene classification from gene sequence data using data mining and machine learning techniques. Our research's main goal is to compare different ma-chine learning approaches based on time of execution, and over-all efficiency by testing them on different micro-array datasets of gene sequence and determining the best approach for gene clas-sification. Eight different machine learning techniques have been tested on eleven different gene expression datasets. We also ap-ply feature selection method before we apply classification techniques on the gene expression datasets. The experimental results show that feature selection method improve the performance of the techniques on the gene expression datasets. Moreover, we per-form pattern analysis on some gene expression datasets using J 48 decision tree outcome.
ISSN:	2160-1348
DOI:	10.1109/ICMLC54886.2021.9737248