Critical Gene Selection by a Modified Particle Swarm Optimization Approach

  • Biswajit Jana
  • Sriyankar AcharyaaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11942)


Gene selection is an eminent area of research in computational biology. Identifying disease critical genes analysing large gene expression dataset is a challenging problem in computational biology. Application of meta-heuristics is considered to be efficient to attempt such problem. Particle Swarm Optimization (PSO) is a meta-heuristic technique based on swarm intelligence. An improved version of PSO, namely, New Variant Repository and Mutation based PSO (NVRMPSO) combined with kNN (NVRMPSO-kNN) has been applied here to find the subset of genes (disease critical) from gene expression dataset. The modification has been done on a recent PSO version, referred as, Repository and Mutation based PSO (RMPSO). The kNN algorithm has been used for sample classification. The performance of proposed NVRMPSO-kNN has been compared with RMPSO incorporated with kNN (RMPSO-kNN) in gene selection problem. The RMPSO-kNN and proposed NVRMPSO-kNN have been applied to three different sizes of datasets, numbered as, E-MEXP-1050, GSE60438 and GSE10588 of preeclampsia disease. The experimental results reveal that the efficacy of NVRMPSO-kNN is better than that of RMPSO-kNN when Classification Accuracy is considered. Furthermore, the performance of proposed NVRMPSO-kNN has been validated with the state-of-the-art observations.


Gene selection Gene expression PSO RMPSO kNN 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Maulana Abul Kalam Azad University of TechnologyKolkataIndia

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