FuncBase : a resource for quantitative gene function annotation
John E. Beaver,Murat Tasan,Francis D. Gibbons,Weidong Tian,Timothy P. Hughes,Frederick P. Roth +5 more
TL;DR: FuncBase is a web resource for viewing quantitative machine learning-based gene function annotations, with Quantitative annotations of genes, including fungal and mammalian genes, with Gene Ontology terms accompanied by a community feedback system.
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Abstract: Summary: Computational gene function prediction can serve to focus experimental resources on high-priority experimental tasks. FuncBase is a web resource for viewing quantitative machine learning-based gene function annotations. Quantitative annotations of genes, including fungal and mammalian genes, with Gene Ontology terms are accompanied by a community feedback system. Evidence underlying function annotations is shown. For example, a custom Cytoscape viewer shows functional linkage graphs relevant to the gene or function of interest. FuncBase provides links to external resources, and may be accessed directly or via links from species-specific databases. Availability: FuncBase as well as all underlying data and annotations are freely available via http://func.med.harvard.edu/
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Paul Shannon,Andrew Markiel,Owen Ozier,Nitin S. Baliga,Jonathan T. Wang,Daniel Ramage,Nada Amin,Benno Schwikowski,Trey Ideker +8 more
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TL;DR: 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, that is efficient enough to be deployed on a modern webserver and as accurate as the leading methods on the MouseFunc I benchmark and a new yeast function prediction benchmark.
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