https://doi.org/10.1140/epjs/s11734-025-01621-z
Review
Open source ML framework algorithms for biophysical models
Informatics and Control System Faculty, Bauman Moscow State Technical University, 2-nd Baumanskaya, 5, Moscow, 105005, Moscow, Russia
Received:
9
March
2025
Accepted:
29
March
2025
Published online:
23
April
2025
Biophysical models for processing multimodal data are mathematical computer program code and algorithms that integrate and process information from various sources (modalities), such as time series (e.g., biochemical indicators, electrocardiograms or electroencephalograms), genetic information, medical images, and other types of data to describe and understand complex biological processes. This article focuses on the application of open source AutoML framework algorithms for analyzing biophysical models. There are a lot of complex AutoML reviews out there. But none of them do not take into account biomedical/biophysical special fine-tuning and functional “out-of-the-box” procedures and code functionalities. It is the import areas such as processing cardiac signals, functional brain response, computer tomography, electronic medical/healthcare record’s textual data, and genetic information. By bridging the gap between machine learning automation and biophysical modeling, biophysical AutoML enables researchers to tackle complex biological problems with greater efficiency and accuracy. Research results confirm that Open source AutoML framework algorithms for biomed/biophysical models could improve diagnostic accuracy, reduce analysis time, and promote the development of personalized medicine, although further improvement of regulatory frameworks and validation methods is required.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
