Opera Medica et Physiologica

Comparison of Machine Learning Methods for Analysis of Ulcerative Colitis Proteomic Data

Author Affiliations

Artem Ryblov1, Sergey Kolesov2, Elvira Fedulova2, Nikolay Karyakin, Mikhail Ivanchenko3, Alexey Zaikin1,3,4 

1 Institute of Supercomputing Technologies, Lobachevsky University, Nizhny Novgorod, Russia;
2 Institute of Paediatrics, Volga Region Federal Medical Research Centre, Ministry of Health Care, Nizhny Novgorod, Russia;
3 Department of Applied Mathematics, Lobachevsky University, Nizhny Novgorod, Russia;
4 Department of Mathematics and Institute for Women’s Health, University College London, United Kingdom.

Corresponding author: 

Alexey Zaikin (alexey.zaikin@ucl.ac.uk)



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.