Classification of Approaches and Challenges of Frequent Subgraphs Mining in Biological Networks
This work provides a review and classification for researchers in bioinformatics and data mining, but it is incremental as it does not introduce new methods or results.
The paper tackles the problem of analyzing large biological networks by classifying existing approaches and challenges for frequent subgraph mining, which is used to recognize common patterns in graph-based models of biological data.
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.