Sparse-Low rank matrix decomposition framework for identifying potential biomarkers for inflammatory bowel disease

Mustafa Alshawaqfeh, Ahmad Al Kawam, Erchin Serpedin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Inflammatory bowel disease (IBD) is a class of uncured chronic diseases which causes severe discomfort and in some cases could lead to life-threatening complications. Recent studies suggest a relationship between IBD and the gut microbiota. These findings reveal potential for identifying bacterial biomarkers for IBD to enable the detection and further investigation into unknown aspects of the disease. This work presents a novel method for identifying microbial biomarkers using robust principal component analysis (RPCA). Our method uses matrix decomposition to separate bacteria exhibiting a difference in abundance between healthy and diseased samples from the bacteria that have not undergone substantial change in abundance. Our method then ranks and identifies the top bacteria to be used as biomarkers. We contrast the proposed method with three well used state-of-the-art bacterial biomarker detection approaches over two datasets in relation to IBD. Our method outperforms the competing methods on the different evaluation cases.

Original languageEnglish
Title of host publication25th European Signal Processing Conference, EUSIPCO 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1882-1886
Number of pages5
Volume2017-January
ISBN (Electronic)9780992862671
DOIs
Publication statusPublished - 23 Oct 2017
Event25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece
Duration: 28 Aug 20172 Sep 2017

Other

Other25th European Signal Processing Conference, EUSIPCO 2017
CountryGreece
CityKos
Period28/8/172/9/17

Fingerprint

Biomarkers
Decomposition
Bacteria
Principal component analysis

ASJC Scopus subject areas

  • Signal Processing

Cite this

Alshawaqfeh, M., Kawam, A. A., & Serpedin, E. (2017). Sparse-Low rank matrix decomposition framework for identifying potential biomarkers for inflammatory bowel disease. In 25th European Signal Processing Conference, EUSIPCO 2017 (Vol. 2017-January, pp. 1882-1886). [8081536] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/EUSIPCO.2017.8081536

Sparse-Low rank matrix decomposition framework for identifying potential biomarkers for inflammatory bowel disease. / Alshawaqfeh, Mustafa; Kawam, Ahmad Al; Serpedin, Erchin.

25th European Signal Processing Conference, EUSIPCO 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 1882-1886 8081536.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Alshawaqfeh, M, Kawam, AA & Serpedin, E 2017, Sparse-Low rank matrix decomposition framework for identifying potential biomarkers for inflammatory bowel disease. in 25th European Signal Processing Conference, EUSIPCO 2017. vol. 2017-January, 8081536, Institute of Electrical and Electronics Engineers Inc., pp. 1882-1886, 25th European Signal Processing Conference, EUSIPCO 2017, Kos, Greece, 28/8/17. https://doi.org/10.23919/EUSIPCO.2017.8081536
Alshawaqfeh M, Kawam AA, Serpedin E. Sparse-Low rank matrix decomposition framework for identifying potential biomarkers for inflammatory bowel disease. In 25th European Signal Processing Conference, EUSIPCO 2017. Vol. 2017-January. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1882-1886. 8081536 https://doi.org/10.23919/EUSIPCO.2017.8081536
Alshawaqfeh, Mustafa ; Kawam, Ahmad Al ; Serpedin, Erchin. / Sparse-Low rank matrix decomposition framework for identifying potential biomarkers for inflammatory bowel disease. 25th European Signal Processing Conference, EUSIPCO 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1882-1886
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