Associating Genes and Protein Complexes with Disease via Network Propagation

By Oron Vanunu, Oded Magger, Eytan Ruppin, Tomer Shlomi, Roded Sharan. PLoS Comput. Biol., 2010

838 citations recorded by the source corpus.

A fundamental challenge in human health is the identification of disease-causing genes. Recently, several studies have tackled this challenge via a network-based approach, motivated by the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. However, most of these approaches use only local network information in the inference process and are restricted to inferring single gene associations. Here, we provide a global, network-based method for prioritizing disease genes and inferring protein complex associations, which we call PRINCE. The method is based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. We exploit this function to predict not only genes but also protein complex associations with a disease of interest. We test our method on gene-disease association data, evaluating both the prioritization achieved and the protein complexes inferred. We show that our method outperforms extant approaches in both tasks. Using data on 1,369 diseases from the OMIM knowledgebase, our method is able (in a cross validation setting) to rank the true causal gene first for 34% of the diseases, and infer 139 disease-related complexes that are highly coherent in terms of the function, expression and conservation of their member proteins. Importantly, we apply our method to study three multi-factorial diseases for which some causal genes have been found already: prostate cancer, alzheimer and type 2 diabetes mellitus. PRINCE's predictions for these diseases highly match the known literature, suggesting several novel causal genes and protein complexes for further investigation.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a9e2d3bd26cc2d952a7502d938f39ba64fba4a52

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Citations in the corpus

Network propagation: a universal amplifier of genetic associations (2017, background): “The first (1D) mode operates by projecting the gene-based scores on a functional, or physical, interaction network, seeking dense regions of this network that span high-scoring genes 74,84,85 .”
Network-based methods for human disease gene prediction. (2011, methodology): “[58] who adopted the same phenotype similarity metric computed by van Driel et al.”
Network Pharmacology Databases for Traditional Chinese Medicine: Review and Assessment (2019, methodology): “Moreover, the PRINCE (Vanunu et al., 2010) algorithm is used to prioritize disease genes and infer protein complex associations, based on formulating constraints on the prioritization function which is related with its smoothness over the network and usage of prior information.”
Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm (2016, methodology): “In our work, we adjust Simr by applying the logistic function which has been used by Vanunu et al. (2010) to modify the phenotypic similarities between diseases associated with genes.”
Network Medicine: A Network-based Approach to Human Disease (2011, methodology): “Variants of this methodology have been applied to detecting disease genes related to a wide range of diseases, from diabetes mellitus to prostate cancer and Alzheimer's diseas”
Review of Drug Repositioning Approaches and Resources (2018, methodology): “[31] DrugNet Propagation Disease-drug-protein A comprehensive propagation method to predict different propagation strategies in different subnets.”
Computational solutions for omics data (2013, background): “Thus, network modularity can be leveraged to carry out disease gene prioritization and to uncover pathways associated with disease through a diverse set of methods 144 , including network flow 145 and random walks 146,147 .”
Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder (2016, methodology): “Traditional disease-gene prediction methods 48 use only genes with the highest confidence or all genes irrespective of the evidence linking them to disease.”
The Emerging Paradigm of Network Medicine in the Study of Human Disease (2012, background): “49 Linkage methods and mapping of disease modules have become much more prevalent, probably due to the relative ease of performing these analyses.”
Network-based stratification of tumor mutations (2013, methodology): “Next, the technique of network propagation 31 is applied to spread the influence of each mutation profile over its network neighborhood ( Fig.”
Prediction of disease genes using tissue-specified gene-gene network (2014, positive): “Phenotype similarity is used as the restart vector in PRINCE [18] and the combination of phenotype similarity and protein proximity is used as the restart vector for ProSim method [33].”