Combining hierarchical and associative gene ontology relations with textual evidence in estimating gene and gene product similarity.

Two approaches have recently emerged where the similarity between two genes or gene products is obtained by comparing Gene Ontology (GO) annotations associated with the genes or gene products. One approach captures GO-based similarity in terms of hierarchical relations within each gene subontology, while the other relies on associative relations across the three gene subontologies. We propose a novel methodology where the two approaches can be merged and enriched by textual evidence extracted from biomedical literature with ensuing benefits in coverage and stronger correlation with sequence-based similarity.

Enriching regulatory networks by bootstrap learning using optimised GO-based gene similarity and gene links mined from PubMed abstracts.

Increasingly, reverse engineering methods have been employed to infer transcriptional regulatory networks from gene expression data. Enrichment with independent evidence from sources such as the biomedical literature and the Gene Ontology (GO) is desirable to corroborate, annotate and expand these networks as well as manually constructed networks. In this paper, we explore a novel approach for computer-assisted enrichment of regulatory networks. GO-based gene similarity is first tuned to an initial network augmented with gene links mined from PubMed and then used to drive network construction using a bootstrapping algorithm. We describe two applications of this approach and discuss its added value in terms of corroboration, annotation and expansion of manually constructed and reversed engineered networks.

Using the gene ontology to enrich biological pathways.

Most current approaches to automatic pathway generation are based on a reverse engineering approach in which pathway plausibility is solely derived from gene expression data and not independently validated. Alternative approaches use prior biological knowledge to validate automatically inferred pathways, but the prior knowledge is usually not sufficiently tuned to the pathology of focus. We present a novel pathway generation approach that combines insights from the reverse engineering and knowledge-based approaches to increase the biological plausibility of automatically generated regulatory networks and describe an application of this approach to transcriptional data from a mouse model of neuroprotection during stroke.