A New Path Length Measure Based on GO for Gene Similarity with Evaluation using SGD Pathways

Abstract

We propose a new method for measuring the semantic similarity of genes based on path length between their annotation terms in the Gene Ontology. Our method applies an exponential transfer function to the average path length between two genes to compute their similarity. The non-linear measure ensures that the semantic similarity decreases with distance and proves to be quite competitive when compared to other measures. The advantage of the proposed measure is its simplicity and ease of implementation which gives it a great appeal in this domain. The measure uses only one feature (path length) for computing the similarity between genes. For validation purposes, we computed the similarity of genes from the Saccharomyces Genome Database (SGD) taking part in various cellular pathways. We analyzed 152 pathways from SGD and compared our similarity results with two of the leading measures. The proposed measure proved to be very competitive in all cases and the clustering results showed that our method is able to surpass the leading methods in certain cases.

Determining the semantic similarities of GO terms based on their existence dependencies

“…Semantic similarity among individual ontology GO terms has been studied extensively [1,3,5,6,7,8,9,12]. Some of these studies [1,6,8,12] rely on counting edges in the graph, where shortest path length is used as a distance measure between two terms in the graph.…”

“…Some of these studies [1,6,8,12] rely on counting edges in the graph, where shortest path length is used as a distance measure between two terms in the graph. Others [3,5,9] exploit the information content (IC) of two terms being compared and of their Lowest Common Ancestor (LCA).…”

“…Semantic similarity among individual ontology GO terms has been studied extensively [1,3,5,6,7,8,9,12]. Despite the success of the techniques proposed in these studies, they suffer recall and precision limitations.…”

Semantic similarity measurement between gene ontology terms based on exclusively inherited shared information

“…Traditional approaches to the semantic similarity measurement between GO terms are generally classified into three categories: edge-based methods [21][22][23][24] (also called structure based approaches), which define the semantic similarity based on the conceptual distance derived from the information related with the length or type of edges in the GO graph; node-based methods [25][26][27][28] (also called annotation-based, information content-based methods), where the nodes and their properties are adopted to compute the information content for similarity; and hybrid methods [29][30][31][32][33], that combine the information content with GO graph structure of GO terms for semantic similarity.…”

From text mining to knowledge mining: An integrated framework of concept extraction and categorization for domain ontology

“…Such measures can be also further categorized into two types: i) edge counting measures and ii) information content based measures. In edge counting measures, the similarity is determined by the path length measures (Euzenat & Shvaiko, 2007;Nagar & Al-Mubaid, 2008;Rada, Mili, Bicknell, & Blettner, 1989) in which shortest path between two concepts are measured. Edge counting measure can also find similarity through depth relative measures (Qin, Lu, Yan, & Wu, 2009;Sussna, 1997;Z.…”