GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Background: Most successful computational approaches for protein function prediction integrate multiple genomics and proteomics data sources to make inferences about the function of unknown proteins. The most accurate of these algorithms have long running times, making them unsuitable for real-time protein function prediction in large genomes. As a result, the predictions of these algorithms are stored in static databases that can easily become outdated. We propose a new algorithm, GeneMANIA, that is as accurate as the leading methods, while capable of predicting protein function in real-time. Results: We use a fast heuristic algorithm, derived from ridge regression, to integrate multiple functional association networks and predict gene function from a single process-specific network using label propagation. Our algorithm is efficient enough to be deployed on a modern webserver and is as accurate as, or more so than, the leading methods on the MouseFunc I benchmark and a new yeast function prediction benchmark; it is robust to redundant and irrelevant data and requires, on average, less than ten seconds of computation time on tasks from these benchmarks. Conclusion: GeneMANIA is fast enough to predict gene function on-the-fly while achieving state-of-the-art accuracy. A prototype version of a GeneMANIA-based webserver is available at

References in zbMATH (referenced in 12 articles )

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  1. Janyasupab, Panisa; Suratanee, Apichat; Plaimas, Kitiporn: Network diffusion with centrality measures to identify disease-related genes (2021)
  2. Nahálková, Jarmila: The molecular mechanisms associated with PIN7, a protein-protein interaction network of seven pleiotropic proteins (2020)
  3. Schmich, Fabian; Kuipers, Jack; Merdes, Gunter; Beerenwinkel, Niko: netprioR: a probabilistic model for integrative hit prioritisation of genetic screens (2019)
  4. Gómez-Vela, Francisco; Rodriguez-Baena, Domingo S.; Vázquez-Noguera, José Luis: Structure optimization for large gene networks based on greedy strategy (2018)
  5. Lum, Oliver; Golden, Bruce; Wasil, Edward: An open-source desktop application for generating arc-routing benchmark instances (2018)
  6. Bag, Susmita; Ramaiah, Sudha; Anbarasu, Anand: fabp4 is central to eight obesity associated genes: a functional gene network-based polymorphic study (2015)
  7. Frasca, Marco; Bertoni, Alberto; Re, Matteo; Valentini, Giorgio: A neural network algorithm for semi-supervised node label learning from unbalanced data (2013)
  8. Cesa-Bianchi, Nicolò; Re, Matteo; Valentini, Giorgio: Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference (2012)
  9. Shiga, Motoki; Mamitsuka, Hiroshi: Efficient semi-supervised learning on locally informative multiple graphs (2012)
  10. Schietgat, Leander; Vens, Celine; Struyf, Jan; Blockeel, Hendrik; Kocev, Dragi; Dzeroski, Saso: Predicting gene function using hierarchical multi-label decision tree ensembles (2010) ioport
  11. Warde-Farley, David; Donaldson, Sylva L.; Comes, Ovi; Zuberi, Khalid; Badrawi, Rashad; Chao, Pauline; Franz, Max; Grouios, Chris; Kazi, Farzana; Lopes, Christian Tannus; Maitland, Anson; Mostafavi, Sara; Montojo, Jason; Shao, Quentin; Wright, George; Bader, Gary D.; Morris, Quaid: The genemania prediction server: biological network integration for gene prioritization and predicting gene function (2010) ioport
  12. Gillis, Jesse; Pavlidis, Paul: A methodology for the analysis of differential coexpression across the human lifespan (2009) ioport