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Comparison of machine learning methods for analysis of ulcerative colitis proteomic data

Ryblov, A; Kolesov, S; Fedulova, E; Karyakin, N; Ivanchenko, M; Zaikin, A; (2017) Comparison of machine learning methods for analysis of ulcerative colitis proteomic data. Opera Medica et Physiologica , 3 (1) pp. 25-29. 10.20388/omp2017.001.0044. Green open access

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Abstract

Ulcerative colitis is a chronic inflammatory disease of the gastrointestinal system, affecting adults and children. Its cause is unknown, and the knowledge of reliable biomarkers is limited, especially for children. That makes the search for new biomarkers and pushing forth the analysis of the available data particularly challenging. We investigate proteomic data from children patients as a promising source, and tackle the problem implementing the recently developed parenclitic network approach to machine learning algorithms that solve classification task for proteomic data from healthy and diseased. We expect our approach to be applicable to other gastrointestinal diseases.

Type: Article
Title: Comparison of machine learning methods for analysis of ulcerative colitis proteomic data
Open access status: An open access version is available from UCL Discovery
DOI: 10.20388/omp2017.001.0044
Publisher version: https://doi.org/10.20388/omp2017.001.0044
Language: English
Additional information: © 2017 the Authors. This is an Open Access publication distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License (https://creativecommons.org/licenses/by/4.0/).
Keywords: bioinformatics, machine learning, data analysis, Network analysis, pediatrics, mass-spectrometry
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Womens Cancer
URI: https://discovery.ucl.ac.uk/id/eprint/10098253
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