Gene Selection for Cancer Classification with the Help of Bees

Overview

Journal BMC Med Genomics

Publisher Biomed Central

Specialty Genetics

Date 2016 Aug 12

PMID 27510562

Citations 6

Authors

Johra Muhammad Moosa

Rameen Shakur

Mohammad Kaykobad

Mohammad Sohel Rahman

Affiliations

Soon will be listed here.

Abstract

Background: Development of biologically relevant models from gene expression data notably, microarray data has become a topic of great interest in the field of bioinformatics and clinical genetics and oncology. Only a small number of gene expression data compared to the total number of genes explored possess a significant correlation with a certain phenotype. Gene selection enables researchers to obtain substantial insight into the genetic nature of the disease and the mechanisms responsible for it. Besides improvement of the performance of cancer classification, it can also cut down the time and cost of medical diagnoses.

Methods: This study presents a modified Artificial Bee Colony Algorithm (ABC) to select minimum number of genes that are deemed to be significant for cancer along with improvement of predictive accuracy. The search equation of ABC is believed to be good at exploration but poor at exploitation. To overcome this limitation we have modified the ABC algorithm by incorporating the concept of pheromones which is one of the major components of Ant Colony Optimization (ACO) algorithm and a new operation in which successive bees communicate to share their findings.

Results: The proposed algorithm is evaluated using a suite of ten publicly available datasets after the parameters are tuned scientifically with one of the datasets. Obtained results are compared to other works that used the same datasets. The performance of the proposed method is proved to be superior.

Conclusion: The method presented in this paper can provide subset of genes leading to more accurate classification results while the number of selected genes is smaller. Additionally, the proposed modified Artificial Bee Colony Algorithm could conceivably be applied to problems in other areas as well.

Citing Articles

A gene selection algorithm for microarray cancer classification using an improved particle swarm optimization.

Nagra A, Haider Khan A, Abubakar M, Faheem M, Rasool A, Masood K Sci Rep. 2024; 14(1):19613.

PMID: 39179674 PMC: 11343852. DOI: 10.1038/s41598-024-68744-6.

A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm.

Azadifar S, Ahmadi A BMC Med Inform Decis Mak. 2021; 21(1):333.

PMID: 34838034 PMC: 8627636. DOI: 10.1186/s12911-021-01696-3.

A comparative study of machine learning and deep learning algorithms to classify cancer types based on microarray gene expression data.

Tabares-Soto R, Orozco-Arias S, Romero-Cano V, Segovia Bucheli V, Rodriguez-Sotelo J, Jimenez-Varon C PeerJ Comput Sci. 2021; 6:e270.

PMID: 33816921 PMC: 7924492. DOI: 10.7717/peerj-cs.270.

ClusterMine: A knowledge-integrated clustering approach based on expression profiles of gene sets.

Li H, Xu Y, Zhu X, Liu Q, Omenn G, Wang J J Bioinform Comput Biol. 2020; 18(3):2040009.

PMID: 32698720 PMC: 8864677. DOI: 10.1142/S0219720020400090.

Deep gene selection method to select genes from microarray datasets for cancer classification.

Alanni R, Hou J, Azzawi H, Xiang Y BMC Bioinformatics. 2019; 20(1):608.

PMID: 31775613 PMC: 6880643. DOI: 10.1186/s12859-019-3161-2.

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