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Integration of meta-analysis and supervised machine learning for pattern recognition in breast cancer using epigenetic data

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journal contribution
posted on 14.07.2021, 06:55 by R Panahi, Esmaeil EbrahimieEsmaeil Ebrahimie, A Niazi, A Afsharifar
Breast cancer is one of the most widespread diseases with high incidence and mortality rate in females. The accurate biomarker discovery for the early detection of patients prone to breast cancer is crucial in the treatment and diagnosis of breast cancer. The current study employed a comprehensive approach to detect an epigenomic data pattern of breast cancer using meta-analysis and machine learning approaches. Meta-analysis is a precise method that combines the results of multiple experiments. On the other hand, integrating and combining the test results through machine learning algorithms can deal with data complexity and heterogeneity. The main purpose of the current study was to discover the patterns of epigenome changes in the treatment and prognosis of breast cancer. NCBI and EBI databases were searched for ChIP-Seq data regarding the effect of the drugs on breast cancer. There were ten investigations carried out, four of which were appropriate meta-analysis. NOV, JUN and ZBTB7A transcription factors were identified as the biomarkers of breast cancer. Finally, pattern recognition was performed using nine different attribute weighting algorithms. Fourteen genes were selected by the majority of attribute weighting algorithms as the most informative genes including KIP, TCF12, ABCC5, HDAC11, IPP, HIST1H2AM, ZNF33B, PHF2, ELAVL3, TBC1D9B, TMEM217, CD34, ARHGEF26, and CENPL. The selected genes play vital roles in the occurrence of neoplasms and breast cancer. In this study, using a combination of meta-analysis and data mining, more comprehensive and reliable information were derived compared to the individual studies.

History

Publication Date

03/06/2021

Journal

Informatics in Medicine Unlocked

Volume

24

Article Number

100629

Pagination

p. 9

Publisher

Elsevier

ISSN

2352-9148

Rights Statement

The Author reserves all moral rights over the deposited text and must be credited if any re-use occurs. Documents deposited in OPAL are the Open Access versions of outputs published elsewhere. Changes resulting from the publishing process may therefore not be reflected in this document. The final published version may be obtained via the publisher’s DOI. Please note that additional copyright and access restrictions may apply to the published version.

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