Classification of Approaches and Challenges of Frequent Subgraphs Mining in Biological Networks

TL;DR

This paper reviews the challenges in frequent subgraph mining within biological networks, classifies existing approaches, and analyzes algorithms based on their methods to address these challenges.

Contribution

It provides a comprehensive classification of approaches and challenges in frequent subgraph mining for biological networks, highlighting current algorithmic strategies.

Findings

Identifies key challenges in biological network subgraph mining

Classifies existing approaches based on challenge types

Analyzes algorithms according to their methodological strategies

Abstract

Understanding the structure and dynamics of biological networks is one of the important challenges in system biology. In addition, increasing amount of experimental data in biological networks necessitate the use of efficient methods to analyze these huge amounts of data. Such methods require to recognize common patterns to analyze data. As biological networks can be modeled by graphs, the problem of common patterns recognition is equivalent with frequent sub graph mining in a set of graphs. In this paper, at first the challenges of frequent subgrpahs mining in biological networks are introduced and the existing approaches are classified for each challenge. then the algorithms are analyzed on the basis of the type of the approach they apply for each of the challenges.