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

slides released under CC BY 4.0

most hetio project websites are offline

  • knowledge graph for drug repurposing
     
  • integrates 29 public resources
    knowledge from millions of studies
     
  • ~50 thousand nodes
    11 types (metanodes)
     
  • ~2.25 million relationships
    24 types (metaedges)

Hetionet v1.0

Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Daniel S Himmelstein, Antoine Lizee, Christine Hessler, Leo Brueggeman, Sabrina L Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, Sergio E Baranzini
eLife (2017) https://doi.org/cdfk

Hetionet metagraph (schema)

metapaths

Fig 2. Heterogeneous network edge prediction methodology. A) We constructed the network according to a schema, called a metagraph, which is composed of metanodes (node types) and metaedges (edge types). B) The network topology connecting a gene and disease node is measured along metapaths (types of paths). Starting on Gene and ending on Disease, all metapaths length three or less are computed by traversing the metagraph. C) A hypothetical graph subset showing select nodes and edges surrounding IRF1 and multiple sclerosis. To characterize this relationship, features are computed that measure the prevalence of a specific metapath between IRF1 and multiple sclerosis. D) Two features (for the GeTlD and GiGaD metapaths) are calculated to describe the relationship between IRF1 and multiple sclerosis. The metric underlying the features is degree-weighted path count (DWPC). First, for the specified metapath, all paths are extracted from the network. Next, each path receives a path-degree product (PDP) measuring its specificity (calculated from node-degrees along the path, Dpath). This step requires a damping exponent (here w = 0.5), which adjusts how severely high-degree paths are downweighted. Finally, the path-degree products are summed to produce the DWPC.

degree-weighted path count

DOI: 10.1371/journal.pcbi.1004259

observations =

compound–disease pairs

features = types of paths

Project Rephetio

Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Daniel S Himmelstein, Antoine Lizee, Christine Hessler, Leo Brueggeman, Sabrina L Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, Sergio E Baranzini
eLife (2017) https://doi.org/cdfk

therapeutic crosspurposing for 209,168 compound–disease pairs
https://het.io/repurpose/

1,538 connected

138 connected

explainability of metapath-based approaches

C. Paths for the selected metapaths are ordered by their path score (limited to 100 paths for each metapath). The user selects 8 paths (1 from a subsequent page of results) to show in the graph visualization and highlights a single path involving ARNT2 for emphasis.

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

degree/study bias

the Achilles' heel of network-based approaches

Hetnet connectivity search provides rapid insights into how biomedical entities are related
Daniel Himmelstein, Michael Zietz, Vincent Rubinetti, Kyle Kloster, Benjamin Heil, Faisal Alquaddoomi, Dongbo Hu, David Nicholson, Yun Hao, Blair Sullivan, Michael Nagle, Casey Greene

GigaScience (2023) https://doi.org/gsd85n

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

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

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

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

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}}

Michael Zietz / zietzm.com

Finishing PhD at Columbia

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

Conclusions

  • knowledge graphs / hetnets can succeed at therapeutic crosspurposing
  • but so can predictions based only a compound's and disease's degree
    • evaluate the performance of the "edge prior"
    • make predictions on permuted (degree-preserving) networks as a baseline
  • study and degree biases are not favorable to rare diseases, but high predictions for a rare disease that are still low predictions overall might be picking up on important non-degree signals
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