期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 128, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2020.104143
关键词
Feature ranking evaluation; Biomedicine application; Tumor data
类别
资金
- Ad Futura Slovene Human Resources Development and Scholarship Fund
- Slovenian Research Agency [P2-0103]
- European Commission through the grants of MAESTRA [FP7-ICT612944]
- European Commission [LifeSciHealth-2005-037260]
The task of biomarker discovery translates to the machine learning task of feature ranking to identify potentially viable targets for addressing specific biological statuses. A methodology for evaluating feature rankings was proposed and successfully demonstrated on datasets related to cancer, identifying a set of viable biomarkers.
The task of biomarker discovery is best translated to the machine learning task of feature ranking. Namely, the goal of biomarker discovery is to identify a set of potentially viable targets for addressing a given biological status. This is aligned with the definition of feature ranking and its goal to produce a list of features ordered by their importance for the target concept. This differs from the task of feature selection (typically used for biomarker discovery) in that it catches viable biomarkers that have redundant or overlapping information with often highly important biomarkers, while with feature selection this is not the case. We propose to use a methodology for evaluating feature rankings to assess the quality of a given feature ranking and to discover the best cut-off point. We demonstrate the effectiveness of the proposed methodology on 10 datasets containing data about embryonal tumors. We evaluate two most commonly used feature ranking algorithms (Random forests and RReliefF) and using the evaluation methodology identifies a set of viable biomarkers that have been confirmed to be related to cancer.
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