Good Practice in Data Analysis and Integration

Data-generating experiments are a central aspect of systems biology, and the increasing complexity of large datasets often requires the use of concomitantly complex statistical data analysis and computational/machine learning modelling methods. However, there is often a lack of awareness about the assumptions and characteristics of the statistical tools used, which can render these studies difficult to interpret correctly.

This Research Topic welcomes manuscripts aimed at defining best practices in statistics, machine learning, and computational modelling analyses. Articles should be concise and illustrate, in simple terms and with examples, the problems and bottlenecks of a given statistical and/or data analysis technique used in Systems Biology applications. Tutorial articles illustrating analysis computational/statistical/modelling pipelines are also accepted.

Examples from literature concerning sub-optimal and/or erroneous use and misuse of statistical techniques are welcome, as long they are presented without author-shaming and with clear indications of how the original analysis can be improved. For these types of contributions please select either General Commentary or Opinion articles. General Commentary articles provide critical comments on a previous publication. Opinion articles, on the other hand, allow authors to contribute viewpoints on the interpretation of recent findings in any research area, value of the methods used, as well as weaknesses and strengths of scientific hypotheses.

Articles should focus on existing tools. Articles dealing with novel methods should be submitted to the Data Model and Integration specialty section of the journal.