Machine learning (ML) has become an essential asset for the life sciences and medicine. We selected 250 articles describing ML applications from 17 journals sampling 26 different fields between 2011 and 2016. Independent evaluation by two readers highlighted three results. First, only half of the articles shared software, 64% shared data and 81% applied any kind of evaluation. Although crucial for ensuring the validity of ML applications, these aspects were met more by publications in lower-ranked journals. Second, the authors’ scientific backgrounds highly influenced how technical aspects were addressed: reproducibility and computational evaluation methods were more prominent with computational co-authors; experimental proofs more with experimentalists. Third, 73% of the ML applications resulted from interdisciplinary collaborations comprising authors from at least two of the three disciplines: computational sciences, biology, and medicine. The results suggested collaborations between computational and experimental scientists to generate more scientifically sound and impactful work integrating knowledge from both domains. Although scientifically more valid solutions and collaborations involving diverse expertise did not correlate with impact factors, such collaborations provide opportunities to both sides: computational scientists are given access to novel and challenging real-world biological data, increasing the scientific impact of their research, and experimentalists benefit from more in-depth computational analyses improving the technical correctness of work.
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Thanks to T. Karl and I. Weise (both TUM) for invaluable help with technical and administrative aspects of this work. Thanks to the TUM Graduate School (in particular Z. Zhang) for organizing the summer school, to the TUM (in particular H. Keidel and W. Herrmann) for substantial support on several levels including financing the summer school, to the Weizmann Institute, Tel Aviv University, Technion and Hebrew University for financial and general support; thanks also to the enlightening talks by D. Cremers (TUM), M. Linial (IAS Israel, Hebrew University), Y. Ofran (Bar-Ilan University); thanks to PubMed for providing easy access to published articles and supporting automatic access; thanks to the maintainers of Biopython for providing excellent code to access various databases and process biological data. Last, but not least, thanks to all maintainers of public databases and to all experimentalists who enabled this analysis by making their data publicly available. This work was supported by grant no. 640508 from the Deutsche Forschungsgemeinschaft (DFG).
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Littmann, M., Selig, K., Cohen-Lavi, L. et al. Validity of machine learning in biology and medicine increased through collaborations across fields of expertise. Nat Mach Intell 2, 18–24 (2020). https://doi.org/10.1038/s42256-019-0139-8
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