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We propose a method for gene expression based analysis of cancer phenotypesincorporating network biology knowledge through unsupervised construction ofcomputational graphs. The structural construction of the computational graphsis driven by the use of topological clustering algorithms on protein-proteinnetworks which incorporate inductive biases stemming from network biologyresearch in protein complex discovery. This structurally constrains thehypothesis space over the possible computational graph factorisation whoseparameters can then be learned through supervised or unsupervised tasksettings. The sparse construction of the computational graph enables thedifferential protein complex activity analysis whilst also interpreting theindividual contributions of genes/proteins involved in each individual proteincomplex. In our experiments analysing a variety of cancer phenotypes, we showthat the proposed methods outperform SVM, Fully-Connected MLP, andRandomly-Connected MLPs in all tasks. Our work introduces a scalable method forincorporating large interaction networks as prior knowledge to drive theconstruction of powerful computational models amenable to introspective study.

Incorporating network based protein complex discovery into automated model construction