Koina: Democratizing machine learning for proteomics research.
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| Názov: | Koina: Democratizing machine learning for proteomics research. |
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| Autori: | Lautenbacher, Ludwig, Yang, Kevin L., Kockmann, Tobias, Panse, Christian, Gabriel, Wassim, Bold, Dulguun, Kahl, Elias, Chambers, Matthew, MacLean, Brendan X., Li, Kai, Yu, Fengchao, Searle, Brian C., Wilburn, Damien Beau, Shahneh, Mohammad Reza Zare, Hong, Yuhui, Tang, Haixu, Wang, Mingxun, Gabriels, Ralf, Bouwmeester, Robbin, Devreese, Robbe |
| Zdroj: | Nature Communications; 11/11/2025, Vol. 16 Issue 1, p1-13, 13p |
| Predmety: | MACHINE learning, PROTEOMICS, DATA analysis, BIOCHEMISTRY, OPEN source software, RESEARCH implementation |
| Abstrakt: | Recent developments in machine learning (ML) and deep learning have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML models are regularly published, the rate at which the community adopts these models is slow. This is in part due to a lack of findability and accessibility of these models as well as the technical challenges involved in incorporating these models into data analysis pipelines and demonstrating their reusability for end-users. Here we show Koina, an open-source decentralized and online-accessible model repository to facilitate publication of ML models. Koina enables ML model usage via an easy-to-use online interface, facilitating the integration of ML models in data analysis pipelines. Using the widely used FragPipe computational platform as an example, we demonstrate how Koina can be integrated with existing proteomics software tools and how these integrations improve data analysis. Koina is an open-source, online platform that simplifies access to machine learning models in proteomics, enabling easier integration into analysis tools and helping researchers adopt and reuse ML models more efficiently. [ABSTRACT FROM AUTHOR] |
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| Databáza: | Complementary Index |
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