A gene ontology inferred from molecular networks

By Janusz Dutkowski, Michael Kramer, Michal A. Surma, R. Balakrishnan, J. Cherry, N. Krogan, Trey Ideker. 2012

190 citations recorded by the source corpus.

Subfields: Molecular Biology, Knowledge Graphs & Representation, Computational Biology & Bioinformatics

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Citations in the corpus

Predicting multicellular function through multi-layer tissue networks (2017, background): “This means that a complex tissue involving a multiscale hierarchy of cellular subsystems is not readily captured by current models (Dutkowski et al. , 2012; Carvunis and Ideker, 2014).”
Using deep learning to model the hierarchical structure and function of a cell (2018, methodology): “…reference database from which we extracted 2,526 cellular subsystems (intracellular components, processes or functions) 9 ; or the Clique-eXtracted Ontology (CliXO), an alternative ontology of similar size inferred from large-scale molecular data sets rather than literature curation 10,11 .”
WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013 (2013, background): “Recent works from the Ideker group (10) suggest that gene networks embed hierarchical structure that is consistent with GO and can extend beyond GO.”
A global interaction network maps a wiring diagram of cellular function (2016, background): “As observed previously (16), this indicates that novel functional organization is embedded within large-scale, unbiased data sets, which may not be captured completely by functional standards, including GO as it is currently organized (14).”
Siri of the cell: what biology could learn from the iPhone. (2014, background): “To address these challenges, we recently investigated whether gene ontologies could be inferred computationally, directly from systematic molecular interaction networks (Dutkowski et al., 2013).”
Inferring gene ontologies from pairwise similarity data (2014, methodology): “In previous work, we developed a multistep algorithm for constructing a Network Extracted Ontology (NeXO), which infers a DAG structure from data summarized in the form of a molecular interaction or gene similarity network (Dutkowski et al ., 2013).”
Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems (2016, methodology): “…ontologies were trained using either the Gene Ontology curated from the Saccharomyces literature (Cherry et al., 2012) (FGO) or a data-driven ontology assembled from Saccharomyces datasets using the method of Network-extracted Ontologies (Dutkowski et al., 2013; Kramer et al., 2014) (FNeXO).”