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

Amr Mohamed AbdelAziz1, Taysir Soliman2, Kareem Kamal A Ghany1,3

  • 1Faculty of Computers and Artificial Intelligence, Beni-Suef University, Egypt.

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|April 9, 2021
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Summary
This summary is machine-generated.

This study introduces a new algorithm, Multi-Objective Whale Optimization Algorithm with Tabu Search (MOWOATS), for analyzing big data from microarrays. The algorithm efficiently clusters genes with similar expression profiles, demonstrating high accuracy and scalability.

Keywords:
Data miningDistributed and parallel computationMicroarray dataSwarm intelligence

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Microarray technology generates large-scale gene expression data, often classified as Big Data.
  • Analyzing this Big Data is crucial for identifying gene correlations and understanding biological processes.
  • Traditional clustering methods struggle with the complexity and volume of microarray data, necessitating advanced approaches.

Purpose of the Study:

  • To propose and evaluate a novel hybrid algorithm, Multi-Objective Whale Optimization Algorithm with Tabu Search (MOWOATS), for analyzing massive microarray datasets.
  • To adapt MOWOATS for parallel processing using Apache Spark over Hadoop clusters to handle Big Data efficiently.
  • To assess the quality and scalability of MOWOATS in clustering gene expression profiles.

Main Methods:

  • Development of a hybrid Multi-Objective Whale Optimization Algorithm with Tabu Search (MOWOATS).
  • Adaptation of MOWOATS for parallel computation on Hadoop clusters using Apache Spark.
  • Implementation of three evaluation functions for robust solution assessment.
  • Evaluation of clustering quality using Silhouette and Davies-Bouldin indices.

Main Results:

  • MOWOATS successfully clustered genes with similar expression profiles from massive microarray datasets.
  • The algorithm demonstrated high-quality clustering, with obtained clusters closely matching original data classes.
  • Scalability analysis showed that MOWOATS's running time was inversely proportional to the number of computing nodes, indicating efficient Big Data processing.

Conclusions:

  • MOWOATS is an efficient and scalable hybrid algorithm for analyzing Big Data generated by microarrays.
  • The parallel implementation on Spark over Hadoop enables effective handling of massive gene expression datasets.
  • The proposed method offers a robust solution for gene expression data clustering, aiding in the identification of biologically relevant gene patterns.