PHILHARMONIC Results for P. damicornis, C. goreaui, and D. melanogaster

PHILHARMONIC is a novel computational approach that couples deep learning de novo network inference with robust unsupervised spectral clustering algorithms to uncover functional relationships and high-level organization in non-model organisms. Our clustering approach allows us to de-noise the predicted network, producing highly informative functional modules. We apply PHILHARMONIC to predict clusters with significant functional coherence in the reef-building coral P. damicornis and its algal symbiont C. goreaui, and the well-annotated fruit fly D. melanogaster.