PHOTONAI-A Python API for rapid machine learning model development.
PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences. It is especially designed to support th...
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Public Library of Science (PLoS)
2021
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oai:doaj.org-article:40c648b5e85943e8970846d859fcaa492021-12-02T20:06:44ZPHOTONAI-A Python API for rapid machine learning model development.1932-620310.1371/journal.pone.0254062https://doaj.org/article/40c648b5e85943e8970846d859fcaa492021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254062https://doaj.org/toc/1932-6203PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences. It is especially designed to support the iterative model development process and automates the repetitive training, hyperparameter optimization and evaluation tasks. Importantly, the workflow ensures unbiased performance estimates while still allowing the user to fully customize the machine learning analysis. PHOTONAI extends existing solutions with a novel pipeline implementation supporting more complex data streams, feature combinations, and algorithm selection. Metrics and results can be conveniently visualized using the PHOTONAI Explorer and predictive models are shareable in a standardized format for further external validation or application. A growing add-on ecosystem allows researchers to offer data modality specific algorithms to the community and enhance machine learning in the areas of the life sciences. Its practical utility is demonstrated on an exemplary medical machine learning problem, achieving a state-of-the-art solution in few lines of code. Source code is publicly available on Github, while examples and documentation can be found at www.photon-ai.com.Ramona LeeningsNils Ralf WinterLucas PlagwitzVincent HolsteinJan ErnstingKelvin SarinkLukas FischJakob SteenwegLeon Kleine-VennekateJulian GebkerDaniel EmdenDominik GrotegerdNils OpelBenjamin RisseXiaoyi JiangUdo DannlowskiTim HahnPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254062 (2021) |
| institution |
DOAJ |
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DOAJ |
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EN |
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Medicine R Science Q |
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Medicine R Science Q Ramona Leenings Nils Ralf Winter Lucas Plagwitz Vincent Holstein Jan Ernsting Kelvin Sarink Lukas Fisch Jakob Steenweg Leon Kleine-Vennekate Julian Gebker Daniel Emden Dominik Grotegerd Nils Opel Benjamin Risse Xiaoyi Jiang Udo Dannlowski Tim Hahn PHOTONAI-A Python API for rapid machine learning model development. |
| description |
PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences. It is especially designed to support the iterative model development process and automates the repetitive training, hyperparameter optimization and evaluation tasks. Importantly, the workflow ensures unbiased performance estimates while still allowing the user to fully customize the machine learning analysis. PHOTONAI extends existing solutions with a novel pipeline implementation supporting more complex data streams, feature combinations, and algorithm selection. Metrics and results can be conveniently visualized using the PHOTONAI Explorer and predictive models are shareable in a standardized format for further external validation or application. A growing add-on ecosystem allows researchers to offer data modality specific algorithms to the community and enhance machine learning in the areas of the life sciences. Its practical utility is demonstrated on an exemplary medical machine learning problem, achieving a state-of-the-art solution in few lines of code. Source code is publicly available on Github, while examples and documentation can be found at www.photon-ai.com. |
| format |
article |
| author |
Ramona Leenings Nils Ralf Winter Lucas Plagwitz Vincent Holstein Jan Ernsting Kelvin Sarink Lukas Fisch Jakob Steenweg Leon Kleine-Vennekate Julian Gebker Daniel Emden Dominik Grotegerd Nils Opel Benjamin Risse Xiaoyi Jiang Udo Dannlowski Tim Hahn |
| author_facet |
Ramona Leenings Nils Ralf Winter Lucas Plagwitz Vincent Holstein Jan Ernsting Kelvin Sarink Lukas Fisch Jakob Steenweg Leon Kleine-Vennekate Julian Gebker Daniel Emden Dominik Grotegerd Nils Opel Benjamin Risse Xiaoyi Jiang Udo Dannlowski Tim Hahn |
| author_sort |
Ramona Leenings |
| title |
PHOTONAI-A Python API for rapid machine learning model development. |
| title_short |
PHOTONAI-A Python API for rapid machine learning model development. |
| title_full |
PHOTONAI-A Python API for rapid machine learning model development. |
| title_fullStr |
PHOTONAI-A Python API for rapid machine learning model development. |
| title_full_unstemmed |
PHOTONAI-A Python API for rapid machine learning model development. |
| title_sort |
photonai-a python api for rapid machine learning model development. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2021 |
| url |
https://doaj.org/article/40c648b5e85943e8970846d859fcaa49 |
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