Disease candidate gene identification and prioritization using protein interaction networks

By J. Chen, Bruce J. Aronow, Anil G. Jegga. BMC Bioinformatics, 2009

374 citations recorded by the source corpus.

BackgroundAlthough most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a candidate gene prioritization method that is entirely based on protein-protein interaction network (PPIN) analyses.ResultsFor the first time, extended versions of the PageRank and HITS algorithms, and the K-Step Markov method are applied to prioritize disease candidate genes in a training-test schema. Using a list of known disease-related genes from our earlier study as a training set ("seeds"), and the rest of the known genes as a test list, we perform large-scale cross validation to rank the candidate genes and also evaluate and compare the performance of our approach. Under appropriate settings – for example, a back probability of 0.3 for PageRank with Priors and HITS with Priors, and step size 6 for K-Step Markov method – the three methods achieved a comparable AUC value, suggesting a similar performance.ConclusionEven though network-based methods are generally not as effective as integrated functional annotation-based methods for disease candidate gene prioritization, in a one-to-one comparison, PPIN-based candidate gene prioritization performs better than all other gene features or annotations. Additionally, we demonstrate that methods used for studying both social and Web networks can be successfully used for disease candidate gene prioritization.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7e95fe562db2eaf30e8e874aa22de9836abd7f83

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Citations in the corpus

The power of protein interaction networks for associating genes with diseases (2010, positive): “Random walks have been used to transfer annotations within networks (Chen et al., 2009; Kohler et al., 2008). Kohler et al. (2008) define a random walk (‘RW’) starting from genes known to be associated with a query disease d. At each time step, the walk has a probability r of returning to the initia”
Leaders in Social Networks, the Delicious Case (2011, background): “In a word, LeaderRank is more tolerant than PageRank against noisy topology, and thus has a high potential in applications on noisy social networks or protein-protein networks [21].”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “Here semantic networks and other combined networks of drug and herb effects and targetsmay offer help in prediction of drug safety (Chen et al., 2009a; Zheng et al., 2012b).”
ToppGene Suite for gene list enrichment analysis and candidate gene prioritization (2009, methodology): “The back end of ToppGene Suite is a scripted process that automatically downloads data from publicly available data sources [see (10, 27 ) for more details].”
Prioritizing candidate disease genes by network-based boosting of genome-wide association data. (2011, result): “…OMIM database (http://omim.org/), shows similarly strong prediction strengths and the same relative ranking of algorithm performance. that ''diffuse'' disease associations across the network, considering both direct and indirect connections, similar to the methods considered in Chen et al. (2009a).”