TELKOMNIKA Telecommunication, Computing, Electronics and Control
The importance of data classification using machine learning methods in microarray data
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
The importance of data classification using machine learning methods in microarray data
The importance of data classification using machine learning methods in microarray data
Subject
Cancers
DNA
Gene expression
Machine learning
Microarrays
RNA
DNA
Gene expression
Machine learning
Microarrays
RNA
Description
The detection of genetic mutations has attracted global attention. several
methods have proposed to detect diseases such as cancers and tumours. One
of them is microarrays, which is a type of representation for gene expression
that is helpful in diagnosis. To unleash the full potential of microarrays,
machine-learning algorithms and gene selection methods can be
implemented to facilitate processing on microarrays and to overcome other
potential challenges. One of these challenges involves high dimensional data
that are redundant, irrelevant, and noisy. To alleviate this problem, this
representation should be simplified. For example, the feature selection
process can be implemented by reducing the number of features adopted in
clustering and classification. A subset of genes can be selected from a pool of
gene expression data recorded on DNA micro-arrays. This paper reviews
existing classification techniques and gene selection methods. The
effectiveness of emerging techniques, such as the swarm intelligence
technique in feature selection and classification in microarrays, are reported
as well. These emerging techniques can be used in detecting cancer. The
swarm intelligence technique can be combined with other statistical methods
for attaining better results.
methods have proposed to detect diseases such as cancers and tumours. One
of them is microarrays, which is a type of representation for gene expression
that is helpful in diagnosis. To unleash the full potential of microarrays,
machine-learning algorithms and gene selection methods can be
implemented to facilitate processing on microarrays and to overcome other
potential challenges. One of these challenges involves high dimensional data
that are redundant, irrelevant, and noisy. To alleviate this problem, this
representation should be simplified. For example, the feature selection
process can be implemented by reducing the number of features adopted in
clustering and classification. A subset of genes can be selected from a pool of
gene expression data recorded on DNA micro-arrays. This paper reviews
existing classification techniques and gene selection methods. The
effectiveness of emerging techniques, such as the swarm intelligence
technique in feature selection and classification in microarrays, are reported
as well. These emerging techniques can be used in detecting cancer. The
swarm intelligence technique can be combined with other statistical methods
for attaining better results.
Creator
Aws Naser Jaber, Kohbalan Moorthy, Logenthiran Machap, Safaai Deris
Source
http://journal.uad.ac.id/index.php/TELKOMNIKA
Date
Aug 29, 2020
Contributor
PERI IRAWAN
Format
PDF
Language
ENGLISH
Type
TEXT
Files
Collection
Citation
Aws Naser Jaber, Kohbalan Moorthy, Logenthiran Machap, Safaai Deris, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
The importance of data classification using machine learning methods in microarray data,” Repository Horizon University Indonesia, accessed February 17, 2025, https://repository.horizon.ac.id/items/show/3659.
The importance of data classification using machine learning methods in microarray data,” Repository Horizon University Indonesia, accessed February 17, 2025, https://repository.horizon.ac.id/items/show/3659.