  Motivation: Understanding functions of proteins in specific human tissues is
essential for insights into disease diagnostics and therapeutics, yet
prediction of tissue-specific cellular function remains a critical challenge
for biomedicine.
  Results: Here we present OhmNet, a hierarchy-aware unsupervised node feature
learning approach for multi-layer networks. We build a multi-layer network,
where each layer represents molecular interactions in a different human tissue.
OhmNet then automatically learns a mapping of proteins, represented as nodes,
to a neural embedding based low-dimensional space of features. OhmNet
encourages sharing of similar features among proteins with similar network
neighborhoods and among proteins activated in similar tissues. The algorithm
generalizes prior work, which generally ignores relationships between tissues,
by modeling tissue organization with a rich multiscale tissue hierarchy. We use
OhmNet to study multicellular function in a multi-layer protein interaction
network of 107 human tissues. In 48 tissues with known tissue-specific cellular
functions, OhmNet provides more accurate predictions of cellular function than
alternative approaches, and also generates more accurate hypotheses about
tissue-specific protein actions. We show that taking into account the tissue
hierarchy leads to improved predictive power. Remarkably, we also demonstrate
that it is possible to leverage the tissue hierarchy in order to effectively
transfer cellular functions to a functionally uncharacterized tissue. Overall,
OhmNet moves from flat networks to multiscale models able to predict a range of
phenotypes spanning cellular subsystems
