A web-based tool to Analyze Semantic Similarity Networks

TL;DR

This paper introduces SSN-Analyzer, a web-based tool designed to construct and analyze Semantic Similarity Networks in biology, demonstrating improved community detection performance on filtered networks for biologically relevant insights.

Contribution

The paper presents a novel web tool for managing and analyzing SSNs, enhancing biological interpretation through network filtering and community detection.

Findings

Filtered networks improve community detection relevance

SSN-Analyzer effectively constructs and preprocesses SSNs

Filtering enhances biological insights from SSNs

Abstract

In computational biology, biological entities such as genes or proteins are usually annotated with terms extracted from Gene Ontology (GO). The functional similarity among terms of an ontology is evaluated by using Semantic Similarity Measures (SSM). More recently, the extensive application of SSMs yielded to the Semantic Similarity Networks (SSNs). SSNs are edge-weighted graphs where the nodes are concepts (e.g. proteins) and each edge has an associated weight that represents the semantic similarity among related pairs of nodes. The analysis of SSNs may reveal biologically meaningful knowledge. For these aims, the need for the introduction of tool able to manage and analyze SSN arises. Consequently we developed SSN-Analyzer a web based tool able to build and preprocess SSN. As proof of concept we demonstrate that community detection algorithms applied to filtered (thresholded) networks, have better performances in terms of biological relevance of the results, with respect to the use of raw unfiltered networks.