Pattern discovery detects statistically significant associations among attribute values known as patterns. Traditional pattern discovery algorithms usually produce overwhelming numbers of overlapping/redundant patterns, weakening their interpretation and decision. Pattern Discovery and Disentanglement (PDD) is a new method that can decompose the entangled associations into groups related to specific factors to overcome this problem. Hence, the patterns discovered are much less in number, yet comprehensive and succinct for machine learning tasks and “explainability”. PDD has a potential for proteomic research, drug discovery, and personalized genetic medicine by revealing subtle genetic/clinical patterns. This chapter provides an overview of the methodology of PDD and its two applications: association discovery on aligned pattern clusters (APCs) and residue-to-residue interactions (R2R-I) prediction. Discovery of patterns from APCs of cytochrome c and class A scavenger receptors are presented as example. Distinct subgroup characteristics of their functional domains and discovery of R2R-I patterns to enhance prediction of residue interactions between binding proteins are discussed.
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