  Machine learning is revolutionizing biology by facilitating the prediction of
outcomes from complex patterns found in massive data sets. Large biological
data sets, like those generated by transcriptome or microbiome studies,measure
many relevant components that interact in vivo with one another in modular
ways.Identifying the high-order interactions that machine learning models use
to make predictions would facilitate the development of hypotheses linking
combinations of measured components to outcome. By using the structure of
random forests, a new algorithmic approach, termed JigSaw,was developed to aid
in the discovery of patterns that could explain predictions made by the forest.
By examining the patterns of individual decision trees JigSaw identifies
high-order interactions between measured features that are strongly associated
with a particular outcome and identifies the relevant decision thresholds.
JigSaw's effectiveness was tested in simulation studies where it was able to
recover multiple ground truth patterns;even in the presence of significant
noise. It was then used to find patterns associated with outcomes in two real
world data sets.It was first used to identify patterns clinical measurements
associated with heart disease. It was then used to find patterns associated
with breast cancer using metabolites measured in the blood. In heart disease,
JigSaw identified several three-way interactions that combine to explain most
of the heart disease records (66%) with high precision (93%). In breast cancer,
three two-way interactions were recovered that can be combined to explain
almost all records (92%) with good precision (79%). JigSaw is an efficient
method for exploring high-dimensional feature spaces for rules that explain
statistical associations with a given outcome and can inspire the generation of
testable hypotheses.
