Current activities of the ELIXIR Machine Learning Focus Group

Abstract

Motivations Machine Learning (ML) has emerged as a discipline that enables computers to assist humans in making sense of large and complex data sets. With the drop in the cost of high-throughput technologies, large amounts of omics data are being generated and made accessible to researchers. Analyzing these complex high-volume data is not trivial, and the use of classical statistics cannot explore their full potential. Machine Learning can thus be very useful in mining large omics datasets to uncover new insights that can consequently lead to the advancement of Life Sciences. Aims The main aims of ELIXIR Machine Learning Focus Group (MLFG) is to increase the reproducibility and transparency of ML methods for researchers, the journal reviewers and the wider community. One of the most pressing issues is to agree to a standardized data structure to describe the most relevant features of ML studies being published. The development of standardized reporting model has the potential to make a major impact in increasing the quality of ML-based publications. Results Our focus group was initiated in October 2019, in order to capture the emerging need in Machine Learning expertise across the network. The main result so far is the DOME Recommendations, a set of community-wide recommendations for reporting supervised machine learning–based analyses applied to biological studies. Tasks Currently the ELIXIR Machine Learning Focus Group is working on three main tasks: (1) using the DOME recommendations to annotate relevant literature in order to gain insights into the level of adherence to DOME; (2) evaluating the gold standard datasets widely used in ML process in order to define and describe the aspects of a gold standard with particular focus on human data; (3) reviewing the efforts around synthetic data in order to establish a set of best practices for their use and application in ML

Authors 5

1. Fotis E Psomopoulos corresponding

   Centre for Research and Technology Hellas

   Affiliation as printed

   Centre for Research and Technology Hellas, Thessaloniki, Greece

2. Emidio Capriotti corresponding

   University of Bologna

   Affiliation as printed

   Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy

3. Núria Queralt-Rosiñach corresponding Aachen

   Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center, Leiden, The Netherlands

4. Leyla Jael Castro
5. Silvio C. E. Tosatto