TELKOMNIKA Telecommunication, Computing, Electronics and Control
A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree
A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree
Subject
Decision tree
Genetic algorithm
KNN
Mosquito anopheles
Ribonucleic acid sequencing
Genetic algorithm
KNN
Mosquito anopheles
Ribonucleic acid sequencing
Description
Malaria larvae accept explosive variable lifecycle as they spread across
numerous mosquito vector stratosphere. Transcriptomes arise in thousands of
diverse parasites. Ribonucleic acid sequencing (RNA-seq) is a prevalent gene
expression that has led to enhanced understanding of genetic queries. RNA-
seq tests transcript of gene expression, and provides methodological
enhancements to machine learning procedures. Researchers have proposed
several methods in evaluating and learning biological data. Genetic algorithm
(GA) as a feature selection process is used in this study to fetch relevant
information from the RNA-Seq Mosquito Anopheles gambiae malaria vector
dataset, and evaluates the results using kth nearest neighbor (KNN) and
decision tree classification algorithms. The experimental results obtained a
classification accuracy of 88.3 and 98.3 percents respectively.
numerous mosquito vector stratosphere. Transcriptomes arise in thousands of
diverse parasites. Ribonucleic acid sequencing (RNA-seq) is a prevalent gene
expression that has led to enhanced understanding of genetic queries. RNA-
seq tests transcript of gene expression, and provides methodological
enhancements to machine learning procedures. Researchers have proposed
several methods in evaluating and learning biological data. Genetic algorithm
(GA) as a feature selection process is used in this study to fetch relevant
information from the RNA-Seq Mosquito Anopheles gambiae malaria vector
dataset, and evaluates the results using kth nearest neighbor (KNN) and
decision tree classification algorithms. The experimental results obtained a
classification accuracy of 88.3 and 98.3 percents respectively.
Creator
Micheal Olaolu Arowolo, Marion Olubunmi Adebiyi, Ayodele Ariyo Adebiyi
Source
http://journal.uad.ac.id/index.php/TELKOMNIKA
Date
Sep 24, 2020
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Micheal Olaolu Arowolo, Marion Olubunmi Adebiyi, Ayodele Ariyo Adebiyi, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree,” Repository Horizon University Indonesia, accessed November 22, 2024, https://repository.horizon.ac.id/items/show/3568.
A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree,” Repository Horizon University Indonesia, accessed November 22, 2024, https://repository.horizon.ac.id/items/show/3568.