A Knowledge-Based Clustering Algorithm Driven by Gene Ontology

Abstract

We have developed an algorithm for inferring the degree of similarity between genes by using the graph-based structure of Gene Ontology (GO). We applied this knowledge-based similarity metric to a clique-finding algorithm for detecting sets of related genes with biological classifications. We also combined it with an expression-based distance metric to produce a co-cluster analysis, which accentuates genes with both similar expression profiles and similar biological characteristics and identifies gene clusters that are more stable and biologically meaningful. These algorithms are demonstrated in the analysis of MPRO cell differentiation time series experiments.

Semantic Similarity in the Gene Ontology

“…1 , although oxygen binding and ion binding are both at a depth of 2, the former is a more specifi c concept and is actually a leaf node. More recent approaches attempt at mitigating some of these issues using for instance the depth of the lowest common ancestor (LCA) [ 11 ], distance to nearest leaf node [ 12 ], and depth of distinct GO subgraphs [ 1 ]. Related approaches, also based on the structure of the ontology, combine distance metrics with node structural properties, such as number of subclasses and distance to the lowest common ancestor between the terms [ 13 ].…”

Characterisation of semantic similarity on gene ontology based on a shortest path approach

“…Edge-based methods are intuitive, among which (Pekar and Staab, 2002) and (Cheng et al, 2004) are two representative ones. Suppose t 1 and t 2 are two terms, and t is their lowest common ancestor.…”

“…The state-of-the-art methods for specifying semantic similarity over the GO terms can be divided into three groups: edge-based, node-based, and a hybrid of the above two. For the edge-based approaches, they mainly consider the lengths of the paths connecting the terms (Cheng et al, 2004;Pekar and Staab, 2002) as the distance between the terms. For the node-based methods, they rely on the properties of the terms derived from information theory (Jiang and Conrath, 1997;Lin, 1998;Resnik, 1999;Schlicker et al, 2006).…”

“…The distance method assumes that the weight of each edge is always 1. Another edge-based algorithm (Cheng et al, 2004) uses the average length of all paths that go through the longest partial path shared by two nodes. The edges are weighted using the depth information.…”

A Novel Measure for Semantic Similarity Computation of Gene Ontology Terms Using Weighted Aggregation of Information Contents

“…These measures also have the "shallow annotation" drawback [6][7][8]: two terms with a certain distance near the root have equal semantic similarity with two terms with the same distance but far from the root. Other edge-based measures [2,9] have attempted to overcome this limitation by assigning different weights to the edges at different graph levels using network density, but they still ignored one fact: GO terms at the same level do not always share same specificity because two terms in the same level can have different gene properties.…”