Semantic similarity analysis of protein data: assessment with biological features and issues

Abstract

The integration of proteomics data with biological knowledge is a recent trend in bioinformatics. A lot of biological information is available and is spread on different sources and encoded in different ontologies (e.g. Gene Ontology). Annotating existing protein data with biological information may enable the use (and the development) of algorithms that use biological ontologies as framework to mine annotated data. Recently many methodologies and algorithms that use ontologies to extract knowledge from data, as well as to analyse ontologies themselves have been proposed and applied to other fields. Conversely, the use of such annotations for the analysis of protein data is a relatively novel research area that is currently becoming more and more central in research. Existing approaches span from the definition of the similarity among genes and proteins on the basis of the annotating terms, to the definition of novel algorithms that use such similarities for mining protein data on a proteome-wide scale. This work, after the definition of main concept of such analysis, presents a systematic discussion and comparison of main approaches. Finally, remaining challenges, as well as possible future directions of research are presented.

Semantic Similarity from Natural Language and Ontology Analysis

“…Several surveys related to the usage of semantic measures in the biomedical domain underline the diversity of their applications, e.g. for diagnosis, disease classification, drug design and gene analysis (Pedersen et al, 2007;Pesquita et al, 2009a;Guzzi et al, 2012). As an illustration, here we focus on applications related to studies on the Gene Ontology (GO) (Ashburner et al, 2000).…”

“…In other cases, semantic measures will be evaluated by analysing the correlation between the semantic similarity of gene conceptual annotations (groups of concepts defined into an ontology) and the score of similarity of gene DNA sequences. Please refer to the work of Guzzi et al (2012) and Pesquita et al (2009a,b) for details related to evaluations in Bioinformatics.…”

Semantic Similarity in the Gene Ontology

“…Guzzi et al [ 9 ] have identifi ed several issues affecting SS measures, which they categorize into external issues, which are usually related to annotation corpora, and internal issues, inherent to the design of the measures. They do however recognize that both kinds of issues can be entangled, for instance when measures make erroneous assumptions about the corpora.…”

“…These include sequence similarity, family similarity, protein-protein interactions, functional modules and complexes, and expression profi le similarity. [ 9 ] GOA-based IC measures were regarded as the best performing measures for most settings, the new wave of more complex structural-based measures, such as SSDD [ 13 ], SORA [ 23 ] and TCSS [ 1 ] are now on the lead, though closely followed by SimGIC. SSDD is based on the concept of semantic "totipotency" whereby terms are assigned values according to their distance to the root and the number of descendants for each of the levels in that path, and then similarity corresponds to the smallest sum of "totipotencies" along a path between two terms.…”

“…Since its inception, CESSM has been adopted by the community and used to evaluate several novel SS measures. The predictive power of SS measures in identifying proteinprotein interactions is also commonly employed in SS evaluation [ 9 ]. In general SS measures are good predictors of PPI, but the most effective are groupwise or maximum combination approach measures.…”