TY - JOUR
T1 - Validity of machine learning in biology and medicine increased through collaborations across fields of expertise
AU - Littmann, Maria
AU - Selig, Katharina
AU - Cohen-Lavi, Liel
AU - Frank, Yotam
AU - Hönigschmid, Peter
AU - Kataka, Evans
AU - Mösch, Anja
AU - Qian, Kun
AU - Ron, Avihai
AU - Schmid, Sebastian
AU - Sorbie, Adam
AU - Szlak, Liran
AU - Dagan-Wiener, Ayana
AU - Ben-Tal, Nir
AU - Niv, Masha Y.
AU - Razansky, Daniel
AU - Schuller, Björn W.
AU - Ankerst, Donna
AU - Hertz, Tomer
AU - Rost, Burkhard
N1 - Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature Limited.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85089606149&partnerID=8YFLogxK
U2 - 10.1038/s42256-019-0139-8
DO - 10.1038/s42256-019-0139-8
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AN - SCOPUS:85089606149
SN - 2522-5839
VL - 2
SP - 18
EP - 24
JO - Nature Machine Intelligence
JF - Nature Machine Intelligence
IS - 1
ER -