Abstract: A processor may receive DNA data associated with a DNA sequence. The processor may classify the DNA sequence as exhibiting circadian behavior utilizing a graph deep learning algorithm. The graph deep learning algorithm may be trained utilizing a combination of features of a human interactome, features of DNA sequences associated with genes identified as exhibiting circadian behavior, and features of DNA sequences associated with genes identified as not exhibiting circadian behavior.

Abstract: Classifying genetic sequences by receiving genetic sequence data according to sequence features associated with gene expression, determining a genetic sequence feature set, determining a first classification for the genetic sequence feature set according to a machine learning model, defining a causal feature set associated with the first classification for the genetic sequence according to the machine learning model, altering the causal feature set for the genetic sequence, yielding an altered causal feature set, determining a second classification for the altered causal feature set according to the machine learning model, wherein the second classification differs from the first classification, and defining a set of target features, wherein the target features include causal features the altered causal feature set.