The power of protein interaction networks for associating genes with diseases

By Saket Navlakha, Carl Kingsford. Bioinform., 2010

379 citations recorded by the source corpus.

Motivation: Understanding the association between genetic diseases and their causal genes is an important problem concerning human health. With the recent influx of high-throughput data describing interactions between gene products, scientists have been provided a new avenue through which these associations can be inferred. Despite the recent interest in this problem, however, there is little understanding of the relative benefits and drawbacks underlying the proposed techniques. Results: We assessed the utility of physical protein interactions for determining gene–disease associations by examining the performance of seven recently developed computational methods (plus several of their variants). We found that random-walk approaches individually outperform clustering and neighborhood approaches, although most methods make predictions not made by any other method. We show how combining these methods into a consensus method yields Pareto optimal performance. We also quantified how a diffuse topological distribution of disease-related proteins negatively affects prediction quality and are thus able to identify diseases especially amenable to network-based predictions and others for which additional information sources are absolutely required. Availability: The predictions made by each algorithm considered are available online at http://www.cbcb.umd.edu/DiseaseNet Contact: carlk@cs.umd.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c59bc12d91e4531e7c1b587acb0c0cf4bd202953

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Citations in the corpus

Network Medicine: A Network-based Approach to Human Disease (2011, background): “A second set of methods assumes that all cellular components that belong to the same topological, functional or disease module have a high likelihood of being involved in the same diseas”
A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information (2017, neutral): “Random walk with restart (RWR), a network diffusion algorithm, has been extensively applied to analyze the complex biological network data [8, 33, 44, 35, 10].”
The Emerging Paradigm of Network Medicine in the Study of Human Disease (2012, background): “Conversely, diffusion methods appear to carry the best predictive power, 50 probably stemming from their ability to integrate a greater amount of relevant topographical information into the final analysis. scale-free (or clustered) network is deﬁned by a power law distribution.”
A DIseAse MOdule Detection (DIAMOnD) Algorithm Derived from a Systematic Analysis of Connectivity Patterns of Disease Proteins in the Human Interactome (2015, methodology): “To evaluate the relative performance of DIAMOnD, we implemented a random walk based algorithm (RW) [35] that was shown to outperform other methods and may therefore serve as a reference [29].”
Integrated interactions database: tissue-specific view of the human and model organism interactomes (2015, background): “Known PPI networks have proven valuable for many applications, including prediction of gene function (1,2), identification of disease genes (3,4) and drug discovery (5,6).”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “…of interactome edge-based disease perturbations may improve the performance of these methods even further in the future (Kohler et al., 2008; Navlakha & Kingsford, 2010; Sharma et al., 2010a; Vanunu et al., 2010; Jiang et al., 2011; Wang et al., 2011a; Cho et al., 2012; Doncheva et al.,…”
Graph Neural Networks and Their Current Applications in Bioinformatics (2021, background): “Navlakha and Kingsford (2010) proved that the network topology method was eﬀective in predicting the association of diseases and even the interaction of biomolecules.”
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 .”
Network-based methods for human disease gene prediction. (2011, background): “Interestingly, the opposite conclusion was drawn by Navlakha and Kingsford [39] as discussed earlier.”
Simplicial closure and higher-order link prediction (2018, background): “But much of the structure in these systems involves higher-order interactions between more than two entities at once (7–11): People often communicate or interact in social groups, not just in pairs; associative relations among ideas or topics often involve the intersection of multiple concepts; and ”
Computational tools for prioritizing candidate genes: boosting disease gene discovery (2012, background): “Such approaches are reviewed in REFS 73,74 .”
Mining breast cancer genes with a network based noise-tolerant approach (2013, background): “disease genes, clustering or graph partitioning to uncover disease modules in the interaction network [10-12].”