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


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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Fig. 1: Spearman correlation coefficients for numeric and binary variables.
Fig. 2: Method validation, comparison and data and programme sharing depends on author expertise.
Fig. 3: Sharing and method comparison hardly impact citations.
Fig. 4: Number of citations and impact factor not consistently higher for collaborations.


  1. 1.

    Bleicher, K. H., Bohm, H. J., Muller, K. & Alanine, A. I. Hit and lead generation: beyond high-throughput screening. Nat. Rev. Drug. Discov. 2, 369–378 (2003).

    Article  Google Scholar 

  2. 2.

    Sulakhe, D. et al. High-throughput translational medicine: challenges and solutions. Adv. Exp. Med. Biol. 799, 39–67 (2014).

    Article  Google Scholar 

  3. 3.

    Howard, J. Quantitative cell biology: the essential role of theory. Mol. Biol. Cell. 25, 3438–3440 (2014).

    Article  Google Scholar 

  4. 4.

    Cook, C. E. et al. The European Bioinformatics Institute in 2016: data growth and integration. Nucl. Acids Res. 44, D20–26 (2016).

    Article  Google Scholar 

  5. 5.

    Chicco, D. Ten quick tips for machine learning in computational biology. BioData Mining 10, 35 (2017).

    Article  Google Scholar 

  6. 6.

    Cios, K. J., Kurgan, L. A. & Reformat, M. Machine learning in the life sciences. IEEE Eng. Med. Biol. Mag. 26, 14–16 (2007).

    Article  Google Scholar 

  7. 7.

    Google Trends. Google (2019).

  8. 8.

    Rost, B., Radivojac, P. & Bromberg, Y. Protein function in precision medicine: deep understanding with machine learning. FEBS Lett. 590, 2327–2341 (2016).

    Article  Google Scholar 

  9. 9.

    Webb, S. Deep learning for biology. Nature 554, 555–557 (2018).

    Article  Google Scholar 

  10. 10.

    Min, S., Lee, B. & Yoon, S. Deep learning in bioinformatics. Brief. Bioinform. 18, 851–869 (2017).

    Google Scholar 

  11. 11.

    Larranaga, P. et al. Machine learning in bioinformatics. Brief. Bioinform. 7, 86–112 (2006).

    Article  Google Scholar 

  12. 12.

    Frank, M. R., Wang, D., Cebrian, M. & Rahwan, I. The evolution of citation graphs in artificial intelligence research. Nat. Mach. Intell. 1, 79–85 (2019).

    Article  Google Scholar 

  13. 13.

    Domingos, P. A few useful things to know about machine learning. Commun. ACM 55, 78–87 (2012).

    Article  Google Scholar 

  14. 14.

    Chou, K.-C. Some remarks on protein attribute prediction and pseudo amino acid composition. J. Theor. Biol. 273, 236–247 (2011).

    MathSciNet  Article  Google Scholar 

  15. 15.

    Ioannidis, J. P. et al. Increasing value and reducing waste in research design, conduct, and analysis. Lancet 383, 166–175 (2014).

    Article  Google Scholar 

  16. 16.

    Gron, A. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (O’Reilly Media, 2017).

  17. 17.

    Chen, S., Arsenault, C. & Larivière, V. Are top-cited papers more interdisciplinary? J. Informetr. 9, 1034–1046 (2015).

    Article  Google Scholar 

  18. 18.

    Cummings, J. & Kiesler, S. Organization theory and the changing nature of science. J. Org. Des. 3, 1–16 (2014).

    Google Scholar 

  19. 19.

    Abramo, G., D’Angelo, C. A. & Di Costa, F. Authorship analysis of specialized vs diversified research output. J. Informetr. 13, 564–573 (2019).

    Article  Google Scholar 

  20. 20.

    Abramo, G., D’Angelo, C. A. & Di Costa, F. Do interdisciplinary research teams deliver higher gains to science? Scientometrics 111, 317–336 (2017).

    Article  Google Scholar 

  21. 21.

    Chen, S., Arsenault, C., Gingras, Y. & Larivière, V. Exploring the interdisciplinary evolution of a discipline: the case of biochemistry and molecular biology. Scientometrics 102, 1307–1323 (2015).

    Article  Google Scholar 

  22. 22.

    Xie, Z., Li, M., Li, J., Duan, X. & Ouyang, Z. Feature analysis of multidisciplinary scientific collaboration patterns based on PNAS. EPJ Data Sci. 7, 5 (2018).

    Article  Google Scholar 

  23. 23.

    Rinia, E. J., van Leeuwen, T. N. & van Raan, A. F. J. Impact measures of interdisciplinary research in physics. Scientometrics 53, 241–248 (2002).

    Article  Google Scholar 

  24. 24.

    Larivière, V. & Gingras, Y. On the relationship between interdisciplinarity and scientific impact. J. Am. Soc. Inform. Sci. Technol. 61, 126–131 (2010).

    Article  Google Scholar 

  25. 25.

    Wallach, J. D., Boyack, K. W. & Ioannidis, J. P. A. Reproducible research practices, transparency, and open access data in the biomedical literature, 2015–2017. PLoS Biol. 16, e2006930 (2018).

    Article  Google Scholar 

  26. 26.

    Berger, B. et al. ISCB’s initial reaction to the New England Journal of Medicine editorial on data sharing. PLoS Comput. Biol. 12, e1004816 (2016).

    Article  Google Scholar 

  27. 27.

    Drazen, J. M. Data sharing and the journal. N. Engl. J. Med. 374, e24 (2016).

    Article  Google Scholar 

  28. 28.

    Longo, D. L. & Drazen, J. M. Data sharing. N. Engl. J. Med. 374, 276–277 (2016).

    Article  Google Scholar 

  29. 29.

    Mind meld. Nature 525, 289–290 (2015).

    Google Scholar 

  30. 30.

    Nissani, M. Ten cheers for interdisciplinarity: the case for interdisciplinary knowledge and research. Soc. Sci. J. 34, 201–216 (1997).

    Article  Google Scholar 

  31. 31.

    van Wesel, M., Wyatt, S. & ten Haaf, J. What a difference a colon makes: how superficial factors. Scientometrics 98, 1601–1615 (2014).

    Article  Google Scholar 

  32. 32.

    Fitzgerald, R. T. & Radmanesh, A. Social media and research visibility. Am. J. Neuroradiol. 36, 637 (2015).

    Article  Google Scholar 

  33. 33.

    Patton, R. M., Stahl, C. G. & Wells, J. C. Measuring scientific impact beyond citation counts. D-Lib Magazine 22, 5 (2016).

    Article  Google Scholar 

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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).

Author information




M.L. and K.S. performed the major part of data analysis and of writing the manuscript. M.L. created and adapted the predefined list of articles. K.S. generated figures and performed statistical tests. L.C. assisted in finding interesting correlations in the data by performing complex analyses and statistical test and in generating figures. M.L., K.S., L.C., Y.F., P.H, E.K., A.M., K.Q., A.R., S.S., A.S., L.S. and A. D.-W. participated in the summer school where the idea for this work was developed, were involved in agreeing on the goals and analysis methods of this work, were involved in data analysis by collecting data from the predefined list of articles, and assisted in writing the manuscript. M.L., K.S. and A.M. collected the data for 2018. N.B.-T., M.Y.N, D.R. and B.W.S. supervised the work over the entire time and proofread the manuscript. D.A. provided valuable comments, especially regarding statistical analysis and was involved in manuscript writing. T.H. and B.R. initiated and supervised the summer school where the idea for this project was developed. T.H. provided important comments to refine the analysis and contributed to manuscript writing. B.R. supervised and guided the work over the entire time and proofread the manuscript. All authors read and approved the final manuscript.

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Correspondence to Maria Littmann or Katharina Selig.

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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).

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