Please use this identifier to cite or link to this item: http://hdl.handle.net/10174/25325

Title: Identification of tissue‐specific tumor biomarker using different optimization algorithms
Authors: Bhowmick, Shib Sankar
Bhattacharjee, Debotosh
Rato, Luis
Editors: Cho, Y.S.
Chung, Y.D.
Keywords: biomarker
machine learning
messenger RNA
optimization
pathway analysis
Issue Date: Dec-2018
Publisher: Springer
Citation: Bhowmick, S.S., Bhattacharjee, D. & Rato, L., Identification of tissue‐specific tumor biomarker using different optimization algorithms, Genes and Genomics, The Genetics Society of Korea, Springer, 2018.
Abstract: Background Identification of differentially expressed genes, i.e., genes whose transcript abundance level differs across different biological or physiological conditions, was indeed a challenging task. However, the inception of transcriptome sequencing (RNA-seq) technology revolutionized the simultaneous measurement of the transcript abundance levels for thousands of genes. Objective In this paper, such next-generation sequencing (NGS) data is used to identify biomarker signatures for several of the most common cancer types (bladder, colon, kidney, brain, liver, lung, prostate, skin, and thyroid) Methods Here, the problem is mapped into the comparison of optimization algorithms for selecting a set of genes that lead to the highest classification accuracy of a two-class classification task between healthy and tumor samples. As the opti- mization algorithms Artificial Bee Colony (ABC), Ant Colony Optimization, Differential Evolution, and Particle Swarm Optimization are chosen for this experiment. A standard statistical method called DESeq2 is used to select differentially expressed genes before being feed to the optimization algorithms. Classification of healthy and tumor samples is done by support vector machine Results Cancer-specific validation yields remarkably good results in terms of accuracy. Highest classification accuracy is achieved by the ABC algorithm for Brain lower grade glioma data is 99.10%. This validation is well supported by a statisti- cal test, gene ontology enrichment analysis, and KEGG pathway enrichment analysis for each cancer biomarker signature Conclusion The current study identified robust genes as biomarker signatures and these identified biomarkers might be helpful to accurately identify tumors of unknown origin
URI: https://doi.org/10.1007/s13258-018-0773-2
http://hdl.handle.net/10174/25325
Type: article
Appears in Collections:INF - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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