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MAINE: a web tool for multi-omics feature selection and rule-based data exploration
Author(s) -
Aleksandra Gruca,
Joanna Henzel,
I. Kostorz,
Tomasz Stęclik,
Łukasz Wróbel,
Marek Sikora
Publication year - 2021
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab862
Subject(s) - feature selection , omics , computer science , data mining , outcome (game theory) , feature (linguistics) , selection (genetic algorithm) , dimensionality reduction , machine learning , data science , artificial intelligence , bioinformatics , biology , mathematics , linguistics , philosophy , mathematical economics
Summary Patient multi-omics datasets are often characterized by a high dimensionality; however, usually only a small fraction of the features is informative, that is change in their value is directly related to the disease outcome or patient survival. In medical sciences, in addition to a robust feature selection procedure, the ability to discover human-readable patterns in the analyzed data is also desirable. To address this need, we created MAINE—Multi-omics Analysis and Exploration. The unique functionality of MAINE is the ability to discover multidimensional dependencies between the selected multi-omics features and event outcome prediction as well as patient survival probability. Learned patterns are visualized in the form of interpretable decision/survival trees and rules. Availability and implementation MAINE is freely available at maine.ibemag.pl as an online web application. Supplementary information Supplementary data are available at Bioinformatics online.

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