Inferring gene ontologies from pairwise similarity data

By Michael Kramer, Janusz Dutkowski, Michael Ku Yu, Vineet Bafna, Trey Ideker. 2014

87 citations recorded by the source corpus.

Motivation: While the manually curated Gene Ontology (GO) is widely used, inferring a GO directly from -omics data is a compelling new problem. Recognizing that ontologies are a directed acyclic graph (DAG) of terms and hierarchical relations, algorithms are needed that: analyze a full matrix of gene–gene pairwise similarities from -omics data; infer true hierarchical structure in these data rather than enforcing hierarchy as a computational artifact; and respect biological pleiotropy, by which a term in the hierarchy can relate to multiple higher level terms. Methods addressing these requirements are just beginning to emerge—none has been evaluated for GO inference. Methods: We consider two algorithms [Clique Extracted Ontology (CliXO), LocalFitness] that uniquely satisfy these requirements, compared with methods including standard clustering. CliXO is a new approach that finds maximal cliques in a network induced by progressive thresholding of a similarity matrix. We evaluate each method’s ability to reconstruct the GO biological process ontology from a similarity matrix based on (a) semantic similarities for GO itself or (b) three -omics datasets for yeast. Results: For task (a) using semantic similarity, CliXO accurately reconstructs GO (>99% precision, recall) and outperforms other approaches (<20% precision, <20% recall). For task (b) using -omics data, CliXO outperforms other methods using two -omics datasets and achieves ∼30% precision and recall using YeastNet v3, similar to an earlier approach (Network Extracted Ontology) and better than LocalFitness or standard clustering (20–25% precision, recall). Conclusion: This study provides algorithmic foundation for building gene ontologies by capturing hierarchical and pleiotropic structure embedded in biomolecular data. Contact: tideker@ucsd.edu

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells (2020, background): “Another opportunity is to structure the DrugCell system hierarchy from ’omics data rather than literature curation (GO), as has previously been done in budding yeast (Kramer et al., 2014; Ma et al., 2018).”
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).”
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 .”