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Machine learning is revolutionizing biology by facilitating the prediction ofoutcomes from complex patterns found in massive data sets. Large biologicaldata sets, like those generated by transcriptome or microbiome studies,measuremany relevant components that interact in vivo with one another in modularways.Identifying the high-order interactions that machine learning models useto make predictions would facilitate the development of hypotheses linkingcombinations of measured components to outcome. By using the structure ofrandom forests, a new algorithmic approach, termed JigSaw,was developed to aidin the discovery of patterns that could explain predictions made by the forest.By examining the patterns of individual decision trees JigSaw identifieshigh-order interactions between measured features that are strongly associatedwith a particular outcome and identifies the relevant decision thresholds.JigSaw's effectiveness was tested in simulation studies where it was able torecover multiple ground truth patterns;even in the presence of significantnoise. It was then used to find patterns associated with outcomes in two realworld data sets.It was first used to identify patterns clinical measurementsassociated with heart disease. It was then used to find patterns associatedwith breast cancer using metabolites measured in the blood. In heart disease,JigSaw identified several three-way interactions that combine to explain mostof the heart disease records (66%) with high precision (93%). In breast cancer,three two-way interactions were recovered that can be combined to explainalmost all records (92%) with good precision (79%). JigSaw is an efficientmethod for exploring high-dimensional feature spaces for rules that explainstatistical associations with a given outcome and can inspire the generation oftestable hypotheses.

JigSaw: A tool for discovering explanatory high-order interactions from random forests