A hybrid multi-objective whale optimization algorithm for analyzing microarray data based on Apache Spark

A microarray is a revolutionary tool that generates vast volumes of data that describe the expression profiles of genes under investigation that can be qualified as Big Data. Hadoop and Spark are efficient frameworks, developed to store and analyze Big Data. Analyzing microarray data helps researche...

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Vydáno v:PeerJ. Computer science Ročník 7; s. e416
Hlavní autoři: AbdelAziz, Amr Mohamed, Soliman, Taysir, Ghany, Kareem Kamal A., Sewisy, Adel
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States PeerJ. Ltd 25.03.2021
PeerJ, Inc
PeerJ Inc
Témata:
Algorithms
Algorithms and Analysis of Algorithms
Analysis
Big Data
Cetacea
Clustering
Computational Biology
Data analysis
Data mining
Data Mining and Machine Learning
Data Science
Datasets
Distributed and parallel computation
Distributed and Parallel Computing
Evaluation
Fibroblasts
Gene expression
Genes
Mathematical optimization
Methods
Microarray data
Mopping
Multiple objective analysis
Optimization
Optimization algorithms
Performance evaluation
Swarm intelligence
Tabu search
Distributed and parallel computation
Swarm intelligence
Data mining
Microarray data
ISSN:2376-5992, 2376-5992
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Shrnutí:A microarray is a revolutionary tool that generates vast volumes of data that describe the expression profiles of genes under investigation that can be qualified as Big Data. Hadoop and Spark are efficient frameworks, developed to store and analyze Big Data. Analyzing microarray data helps researchers to identify correlated genes. Clustering has been successfully applied to analyze microarray data by grouping genes with similar expression profiles into clusters. The complex nature of microarray data obligated clustering methods to employ multiple evaluation functions to ensure obtaining solutions with high quality. This transformed the clustering problem into a Multi-Objective Problem (MOP). A new and efficient hybrid Multi-Objective Whale Optimization Algorithm with Tabu Search (MOWOATS) was proposed to solve MOPs. In this article, MOWOATS is proposed to analyze massive microarray datasets. Three evaluation functions have been developed to ensure an effective assessment of solutions. MOWOATS has been adapted to run in parallel using Spark over Hadoop computing clusters. The quality of the generated solutions was evaluated based on different indices, such as Silhouette and Davies–Bouldin indices. The obtained clusters were very similar to the original classes. Regarding the scalability, the running time was inversely proportional to the number of computing nodes.
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ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.416