degree-weighted path count

DOI: 10.1371/journal.pcbi.1004259

explainability of metapath-based approaches

predictions can be decomposed into their component metapath and path contibutions

Comparison to EveryCure

• Project Rephetio performed therapeutic crosspurposing
• Hetionet designed for
  • approved drugs
  • common, complex diseases
• Rare diseases are difficult to link in networks, but the time is right:
  • genetics
  • phenotypes / symptoms
  • expression
  • literature study
  • NCATS knowlege graphs

A Proteome-Scale Map of the Human Interactome Network

Thomas Rolland, Murat Taşan, Benoit Charloteaux, Samuel J Pevzner, Quan Zhong, Nidhi Sahni, Song Yi, Irma Lemmens, Celia Fontanillo, Roberto Mosca, … Marc Vidal
Cell (2014-11) https://doi.org/f3mn6x

Figure 4A: Adjacency matrices showing Lit-BM-13 (blue) and HI-II-14 (purple) interactions, with proteins in bins of ∼350 and ordered by number of publications along both axes. The color intensity of each square reflects the total number of interactions for the corresponding bins

study bias

• systematic omics-scale data is ideal
• degree bias in networks often arises from study bias rather than the ground truth

The edge prior was not able to predict the separate PPI network better than by random guessing (AUROC of roughly 0.5). Only slightly better was its performance in predicting the separate TF-TG network, at an AUROC of 0.59. We find superior performance in predicting the coauthorship relationships (AUROC 0.75), which was expected as the network being predicted shared roughly the same degree distribution as the network on which the edge prior was computed

The probability of edge existence due to node degree: a baseline for network-based predictions
Michael Zietz, Daniel Himmelstein, Kyle Kloster, Christopher Williams, Michael Nagle, Casey Greene

GigaScience (2024) https://doi.org/gtcbks

empirical approximation of the edge prior

• create many randomized (permuted) networks and count the proportion with an edge.
• degree-preserving permutations using XSwap
• IndeCut by Koslicki & co evaluates several methods
• bonus: metrics can be computed on permuted networks to form a degree baseline

1,206 compound–disease metapaths (length ≤ 4)

1. Upper tier:
   traditional pharmacology
2. Upper-middle tier:
   traditionally biomedicine, but newer in drug efficacy
3. Lower-middle tier:
   genome-wide / high-throughput data sources
4. Lower tier:
   cellular components

Browse at het.io/repurpose/metapaths.html

DWPC Δ AUROC: performance of a metapath on the real network minus performance on permuted networks