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 effectiveness…
Verfasserangaben: | Cindy PerscheidORCiDGND |
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URN: | urn:nbn:de:kobv:517-opus4-548943 |
DOI: | https://doi.org/10.25932/publishup-54894 |
Titel des übergeordneten Werks (Deutsch): | Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät |
Schriftenreihe (Bandnummer): | Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät (010) |
Verlag: | Universitätsverlag Potsdam |
Verlagsort: | Potsdam |
Publikationstyp: | Postprint |
Sprache: | Englisch |
Datum der Erstveröffentlichung: | 02.05.2022 |
Erscheinungsjahr: | 2021 |
Veröffentlichende Institution: | Universität Potsdam |
Datum der Freischaltung: | 02.05.2022 |
Freies Schlagwort / Tag: | Feature selection; Gene expression; Prior knowledge; Reproducible benchmarking |
Seitenanzahl: | 15 |
Erste Seite: | 1 |
Letzte Seite: | 15 |
Organisationseinheiten: | Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH |
DDC-Klassifikation: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
Peer Review: | Referiert |
Publikationsweg: | Open Access / Green Open-Access |
Lizenz (Deutsch): | CC-BY - Namensnennung 4.0 International |
Externe Anmerkung: | Bibliographieeintrag der Originalveröffentlichung/Quelle |