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Comprior

  • Background Reproducible benchmarking is important for assessing the effectiveness of novel feature selection approaches applied on gene expression data, especially for prior knowledge approaches that incorporate biological information from online knowledge bases. However, no full-fledged benchmarking system exists that is extensible, provides built-in feature selection approaches, and a comprehensive result assessment encompassing classification performance, robustness, and biological relevance. Moreover, the particular needs of prior knowledge feature selection approaches, i.e. uniform access to knowledge bases, are not addressed. As a consequence, prior knowledge approaches are not evaluated amongst each other, leaving open questions regarding their effectiveness. Results We present the Comprior benchmark tool, which facilitates the rapid development and effortless benchmarking of feature selection approaches, with a special focus on prior knowledge approaches. Comprior is extensible by custom approaches, offers built-inBackground Reproducible benchmarking is important for assessing the effectiveness of novel feature selection approaches applied on gene expression data, especially for prior knowledge approaches that incorporate biological information from online knowledge bases. However, no full-fledged benchmarking system exists that is extensible, provides built-in feature selection approaches, and a comprehensive result assessment encompassing classification performance, robustness, and biological relevance. Moreover, the particular needs of prior knowledge feature selection approaches, i.e. uniform access to knowledge bases, are not addressed. As a consequence, prior knowledge approaches are not evaluated amongst each other, leaving open questions regarding their effectiveness. Results We present the Comprior benchmark tool, which facilitates the rapid development and effortless benchmarking of feature selection approaches, with a special focus on prior knowledge approaches. Comprior is extensible by custom approaches, offers built-in standard feature selection approaches, enables uniform access to multiple knowledge bases, and provides a customizable evaluation infrastructure to compare multiple feature selection approaches regarding their classification performance, robustness, runtime, and biological relevance. Conclusion Comprior allows reproducible benchmarking especially of prior knowledge approaches, which facilitates their applicability and for the first time enables a comprehensive assessment of their effectivenessshow moreshow less

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Metadaten
Author details:Cindy PerscheidORCiDGND
DOI:https://doi.org/10.1186/s12859-021-04308-z
ISSN:1471-2105
Pubmed ID:https://pubmed.ncbi.nlm.nih.gov/34384353
Title of parent work (English):BMC Bioinformatics
Subtitle (English):Facilitating the implementation and automated benchmarking of prior knowledge-based feature selection approaches on gene expression data sets
Publisher:Springer Nature
Place of publishing:London
Publication type:Article
Language:English
Date of first publication:2021/08/12
Publication year:2021
Release date:2022/05/02
Tag:Feature selection; Gene expression; Prior knowledge; Reproducible benchmarking
Volume:22
Article number:401 (2021)
Number of pages:15
First page:1
Last Page:15
Organizational units:Extern / Extern
Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH
DDC classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Peer review:Referiert
Grantor:Publikationsfonds der Universität Potsdam
Publishing method:Open Access / Gold Open-Access
License (German):License LogoCC-BY - Namensnennung 4.0 International
External remark:Zweitveröffentlichung in der Schriftenreihe Postprints der Universität Potsdam : Reihe der Digital Engineering Fakultät ; 010
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