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Breast and prostate cancer expression similarity analysis by iterative SVM based ensemble gene selection

Published: 01 November 2013 Publication History

Abstract

Epidemiologic and phenotypic evidences indicate that breast and prostate cancers have high pathological similarities. Analysis of pathological similarities between cancers can be beneficial in several aspects such as enabling the knowledge transfer between the cancer studies. To gain knowledge of the similarity between the breast and prostate cancer pathology, common genes that are affected by the two carcinomas are investigated. Gene expression data extracted from RNA-seq experiments, provided through TCGA consortium, is used for gene selection. Gene selection was performed using an iterative SVM based ensemble feature selection approach. Iterative SVM-based gene selection methods enable correlated gene expressions to be considered simultaneously and ensemble approach stabilizes the selection. As results of the analysis, two genes, Transglutaminase 4 (TGM4) and complement component 4A (C4A), were selected as commonly altered genes. Direct relationships of the two genes to the two cancers are not confirmed. However, TGM4 is known to be associated with adenocarcinomas and C4A with ovarian cancer. Thus provides evidence that they maybe pathologically important genes for the two cancers.

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Cited By

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  • (2020)Reviewing Data Analytics Techniques in Breast Cancer TreatmentTrends and Innovations in Information Systems and Technologies10.1007/978-3-030-45697-9_7(65-75)Online publication date: 18-May-2020
  • (2013)DTMBIO 2013Proceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2505814(2555-2556)Online publication date: 27-Oct-2013

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  1. Breast and prostate cancer expression similarity analysis by iterative SVM based ensemble gene selection

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            cover image ACM Conferences
            DTMBIO '13: Proceedings of the 7th international workshop on Data and text mining in biomedical informatics
            November 2013
            38 pages
            ISBN:9781450324199
            DOI:10.1145/2512089
            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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            Publication History

            Published: 01 November 2013

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            Author Tags

            1. breast cancer
            2. gene selection
            3. prostate cancer
            4. rna-seq

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            DTMBIO '13 Paper Acceptance Rate 11 of 18 submissions, 61%;
            Overall Acceptance Rate 41 of 247 submissions, 17%

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            View all
            • (2020)Reviewing Data Analytics Techniques in Breast Cancer TreatmentTrends and Innovations in Information Systems and Technologies10.1007/978-3-030-45697-9_7(65-75)Online publication date: 18-May-2020
            • (2013)DTMBIO 2013Proceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2505814(2555-2556)Online publication date: 27-Oct-2013

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