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Comprior: facilitating the implementation and automated benchmarking of prior knowledge-based feature selection approaches on gene expression data sets

  • 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
URN:urn:nbn:de:kobv:517-opus4-548943
DOI:https://doi.org/10.25932/publishup-54894
Title of parent work (German):Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät
Publication series (Volume number):Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät (010)
Publisher:Universitätsverlag Potsdam
Place of publishing:Potsdam
Publication type:Postprint
Language:English
Date of first publication:2022/05/02
Publication year:2021
Publishing institution:Universität Potsdam
Release date:2022/05/02
Tag:Feature selection; Gene expression; Prior knowledge; Reproducible benchmarking
Number of pages:15
First page:1
Last Page:15
Organizational units:Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH
DDC classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Peer review:Referiert
Publishing method:Open Access / Green Open-Access
License (German):License LogoCC-BY - Namensnennung 4.0 International
External remark:Bibliographieeintrag der Originalveröffentlichung/Quelle
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