Search on Ideker Lab - UC San Diego's publications

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 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 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 This study provides algorithmic foundation for building gene ontologies by capturing hierarchical and pleiotropic structure embedded in biomolecular data The Author 2014 Published by Oxford University Press

Publication search

Inferring gene ontologies from pairwise similarity data.