Shib Sankar Bhowmick1, Debotosh Bhattacharjee2, Luis Rato3. 1. Department of Electronics and Communication Engineering, Heritage Institute of Technology, Kolkata, 700107, India. shibsankar.bhowmick@heritageit.edu. 2. Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India. 3. Department of Informatics, University of Evora, 7004-516, Evora, Portugal.
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 optimization 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 statistical 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.
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 optimization 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 statistical 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.
Authors: V R Iyer; M B Eisen; D T Ross; G Schuler; T Moore; J C Lee; J M Trent; L M Staudt; J Hudson; M S Boguski; D Lashkari; D Shalon; D Botstein; P O Brown Journal: Science Date: 1999-01-01 Impact factor: 47.728
Authors: Jacques Lapointe; Chunde Li; John P Higgins; Matt van de Rijn; Eric Bair; Kelli Montgomery; Michelle Ferrari; Lars Egevad; Walter Rayford; Ulf Bergerheim; Peter Ekman; Angelo M DeMarzo; Robert Tibshirani; David Botstein; Patrick O Brown; James D Brooks; Jonathan R Pollack Journal: Proc Natl Acad Sci U S A Date: 2004-01-07 Impact factor: 11.205