Benchmarking network propagation methods for disease gene identification

• Published in: PLoS computational biology Vol. 15; no. 9; p. e1007276
• Main Authors: Picart-Armada, Sergio, Barrett, Steven J., Willé, David R., Perera-Lluna, Alexandre, Gutteridge, Alex, Dessailly, Benoit H.
• Format: Journal Article Publication
• Language: English
• Published: United States Public Library of Science 01.09.2019; Public Library of Science (PLoS)
• Subjects: Algorithms; Artificial intelligence; Benchmarking; Bioengineering; Bioinformatics; Biologia molecular; Biology and Life Sciences; Biomedical materials; Biomedical research; Business performance management; Ciències de la salut; Computational Biology - methods; Computer and Information Sciences; Computer Simulation; Databases, Genetic; Design factors; Diffusion; Disease; Disease - genetics; Diseases; Drug discovery; Drug Discovery - methods; Drug therapy; Generalized linear models; Genes; Genetic aspects; Genetic research; Genetics; Genomes; Gens; Humans; Identification; Identification and classification; Identification methods; Information systems; Learning algorithms; Machine Learning; Malalties; Medicine and Health Sciences; Methods; Mètodes estadístics; Networks; Performance measurement; Pharmaceutical industry; Physical Sciences; Propagation; Protein seeding; Proteins; R&D; Research & development; Research and Analysis Methods; Software; Spain; Supervision; Target recognition; Àrees temàtiques de la UPC; Spain; United Kingdom > UK
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1007276

In-silico identification of potential target genes for disease is an essential aspect of drug target discovery. Recent studies suggest that successful targets can be found through by leveraging genetic, genomic and protein interaction information. Here, we systematically tested the ability of 12 varied algorithms, based on network propagation, to identify genes that have been targeted by any drug, on gene-disease data from 22 common non-cancerous diseases in OpenTargets. We considered two biological networks, six performance metrics and compared two types of input gene-disease association scores. The impact of the design factors in performance was quantified through additive explanatory models. Standard cross-validation led to over-optimistic performance estimates due to the presence of protein complexes. In order to obtain realistic estimates, we introduced two novel protein complex-aware cross-validation schemes. When seeding biological networks with known drug targets, machine learning and diffusion-based methods found around 2-4 true targets within the top 20 suggestions. Seeding the networks with genes associated to disease by genetics decreased performance below 1 true hit on average. The use of a larger network, although noisier, improved overall performance. We conclude that diffusion-based prioritisers and machine learning applied to diffusion-based features are suited for drug discovery in practice and improve over simpler neighbour-voting methods. We also demonstrate the large impact of choosing an adequate validation strategy and the definition of seed disease genes.