R.ROSETTA: an interpretable machine learning framework

BMC Bioinformatics

Mateusz Garbulowski¹  Karolina Smolińska¹  Susanne Bornelöv²  Patricia Stoll³  Klev Diamanti⁴  Nicholas Baltzer⁵  Jan Komorowski⁶  Lars Feuk⁷  Aleksander Øhrn⁸ 

[1] Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden;Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden;Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK;Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden;Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland;Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden;Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden;Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden;Department of Research, Cancer Registry of Norway, Oslo, Norway;Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden;Swedish Collegium for Advanced Study, Uppsala, Sweden;Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland;Washington National Primate Research Center, Seattle, WA, USA;Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden;Department of Informatics, University of Oslo, Oslo, Norway;

关键词: Transcriptomics;    Interpretable machine learning;    Big data;    Rough sets;    Rule-based classification;    R package;   

DOI  :  10.1186/s12859-021-04049-z

来源: Springer

【 摘 要 】

BackgroundMachine learning involves strategies and algorithms that may assist bioinformatics analyses in terms of data mining and knowledge discovery. In several applications, viz. in Life Sciences, it is often more important to understand how a prediction was obtained rather than knowing what prediction was made. To this end so-called interpretable machine learning has been recently advocated. In this study, we implemented an interpretable machine learning package based on the rough set theory. An important aim of our work was provision of statistical properties of the models and their components.ResultsWe present the R.ROSETTA package, which is an R wrapper of ROSETTA framework. The original ROSETTA functions have been improved and adapted to the R programming environment. The package allows for building and analyzing non-linear interpretable machine learning models. R.ROSETTA gathers combinatorial statistics via rule-based modelling for accessible and transparent results, well-suited for adoption within the greater scientific community. The package also provides statistics and visualization tools that facilitate minimization of analysis bias and noise. The R.ROSETTA package is freely available at https://github.com/komorowskilab/R.ROSETTA. To illustrate the usage of the package, we applied it to a transcriptome dataset from an autism case–control study. Our tool provided hypotheses for potential co-predictive mechanisms among features that discerned phenotype classes. These co-predictors represented neurodevelopmental and autism-related genes.ConclusionsR.ROSETTA provides new insights for interpretable machine learning analyses and knowledge-based systems. We demonstrated that our package facilitated detection of dependencies for autism-related genes. Although the sample application of R.ROSETTA illustrates transcriptome data analysis, the package can be used to analyze any data organized in decision tables.

【 授权许可】

CC BY