Validity of machine learning in biology and medicine increased through collaborations across fields of expertise

Maria Littmann*, Katharina Selig, Liel Cohen-Lavi, Yotam Frank, Peter Hönigschmid, Evans Kataka, Anja Mösch, Kun Qian, Avihai Ron, Sebastian Schmid, Adam Sorbie, Liran Szlak, Ayana Dagan-Wiener, Nir Ben-Tal, Masha Y. Niv, Daniel Razansky, Björn W. Schuller, Donna Ankerst, Tomer Hertz, Burkhard Rost

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

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.

Original languageAmerican English
Pages (from-to)18-24
Number of pages7
JournalNature Machine Intelligence
Volume2
Issue number1
DOIs
StatePublished - 1 Jan 2020

Bibliographical note

Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature Limited.

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