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

Genome-wide systematic PPI method

Literature derived PPI method

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

compound × disease, both with 1 treatment: prior = 0.12%

methotrexate × hypertension = 80% prior probability of treatment

The prior predicted in-sample treatments with AUROC = 97.9% but under-performed on validations:

54.1% on DrugCentral
62.5% on clinical trials

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

For all biomedical networks we've seen, degree is highly predictive of whether an edge exists, but it rarely generalizes to independent validation.

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

analytical approximation of the edge prior — Pᵢ,ⱼ
probability that an edge exists solely based on degree

m = total number of edges in the network
uᵢ = source node degree
vⱼ = target node degree

P_{i,j} = \frac{u_i v_j}{\sqrt{(u_i v_j)^2 + (m - u_i - v_j + 1)^2}}

P_{i,j} = \frac{u_i v_j}{\sqrt{(u_i v_j)^2 + (m - u_i - v_j + 1)^2}}

Michael Zietz / zietzm.com

Finishing PhD at Columbia

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

Upper tier:
traditional pharmacology
Upper-middle tier:
traditionally biomedicine, but newer in drug efficacy
Lower-middle tier:
genome-wide / high-throughput data sources
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

Daniel Himmelstein, PhD

Data-Driven Drug Repurposing Virtual Workshop Series
Deep dive into the challenges and potential applications of knowledge graphs
2024-02-12

Metapath-based approaches for therapeutic crosspurposing and the challenge of degree/study bias

slides.com/dhimmel/dddr-workshop

Hetionet v1.0

Hetionet metagraph (schema)

metapaths

degree-weighted path count

Project Rephetio

explainability of metapath-based approaches

Comparison to EveryCure

degree/study bias

the Achilles' heel of network-based approaches