Gene Expression Data Classification and Pattern Analysis using Data Driven Approach

Aiman Jabeen Ramisa, Ananna Hossain, Sk Md Injamul Islam, Ponuel Mollah Swadesh, Md. Toushif Islam, Md Anisur Rahman, Mohammad Zavid Parvez

Research output: Book chapter/Published conference paperConference paperpeer-review


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 knowledge from diverse available sources. Since
then, it has been used for various science fields, including the
discovery of new drugs, identification of protein coded genes by
analyzing and separating exons from the main sequence, phenotype
prediction based on gene expression. 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 machine learning
approaches based on time of execution, and overall efficiency by
testing them on different micro-array datasets of gene sequence
and determining the best approach for gene classification. Eight
different machine learning techniques have been tested on
eleven different gene expression datasets. We also apply 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
perform pattern analysis on some gene expression datasets using
J48 decision tree outcome.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Machine Learning and Cybernetics (ICMLC)
Publication statusAccepted/In press - 08 Nov 2021


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