Focus |

Reporting standards for machine learning in biology

Machine learning-based approaches are being increasingly applied in life sciences research. This series of articles propose community reporting standards, intended to help improve the reproducibility and useability of machine learning-based analyses.


Community-driven initiatives are proposing standards to improve the reporting and reproducibility of machine learning in biology. We support these developments, some of which are described in this month’s special issue.

Editorial | | Nature Methods


To make machine-learning analyses in the life sciences more computationally reproducible, we propose standards based on data, model and code publication, programming best practices and workflow automation. By meeting these standards, the community of researchers applying machine-learning methods in the life sciences can ensure that their analyses are worthy of trust.

Comment | | Nature Methods

Deep learning algorithms are powerful tools for analyzing, restoring and transforming bioimaging data. One promise of deep learning is parameter-free one-click image analysis with expert-level performance in a fraction of the time previously required. However, as with most emerging technologies, the potential for inappropriate use is raising concerns among the research community. In this Comment, we discuss key concepts that we believe are important for researchers to consider when using deep learning for their microscopy studies. We describe how results obtained using deep learning can be validated and propose what should, in our view, be considered when choosing a suitable tool. We also suggest what aspects of a deep learning analysis should be reported in publications to ensure reproducibility. We hope this perspective will foster further discussion among developers, image analysis specialists, users and journal editors to define adequate guidelines and ensure the appropriate use of this transformative technology.

Comment | | Nature Methods