Grasping frequent subgraph mining for bioinformatics applications

Abstract

Searching for interesting common subgraphs in graph data is a well-studied problem in data mining. Subgraph mining techniques focus on the discovery of patterns in graphs that exhibit a specific network structure that is deemed interesting within these data sets. The definition of which subgraphs are interesting and which are not is highly dependent on the application. These techniques have seen numerous applications and are able to tackle a range of biological research questions, spanning from the detection of common substructures in sets of biomolecular compounds, to the discovery of network motifs in large-scale molecular interaction networks. Thus far, information about the bioinformatics application of subgraph mining remains scattered over heterogeneous literature. In this review, we provide an introduction to subgraph mining for life scientists. We give an overview of various subgraph mining algorithms from a bioinformatics perspective and present several of their potential biomedical applications.Electronic supplementary materialThe online version of this article (10.1186/s13040-018-0181-9) contains supplementary material, which is available to authorized users.

Mining frequent approximate patterns in large networks

“…In [20], the authors introduced two potential extensions of the previously mentioned AGraP algorithm, as a means of reducing output set sizes. A new variation of AGraP was proposed (CloseAFG) as well as a new algorithm (IntAFG).…”

“…Our proposed FPM algorithm is introduced in Section 4, and our experimental results using it are detailed in Section 5. Our conclusions on applications 22,23 of the proposed algorithm and our thoughts on directions for future work 24,25 are the focus of Section 6.…”

“…In the literature, several works have attempted to identify frequent patterns in a large graph through the use of deep first search (DFS) mechanisms and have focused on identifying recurring patterns following a pattern growth strategy. 15,24,26 In recent works, frequent approximate pattern mining is proposed as an extension of existing FPM algorithms. 15,21,24,[27][28][29][30][31][32] A major challenge is to find approximate occurrences of patterns in single oriented and large graphs.…”

“…15,24,26 In recent works, frequent approximate pattern mining is proposed as an extension of existing FPM algorithms. 15,21,24,[27][28][29][30][31][32] A major challenge is to find approximate occurrences of patterns in single oriented and large graphs. 12 Finding patterns might be easy for graph isomorphism, but not for mining approximate patterns in a large graph.…”

Efficiently Mining Recurrent Substructures from Protein Three-Dimensional Structure Graphs

“…The investigation of the proteins' spatial shape may provide important functional and structural insights (Clark et al, 1991;Cootes et al, 2003). Indeed, protein structures have been interpreted as graphs of amino acids and studied based on graph theory concepts (Borgwardt et al, 2005;Anchuri et al, 2013;Dhifli et al, 2014;Dhifli and Nguifo, 2015;Dhifli et al, 2017;Saha et al, 2017;Mrzic et al, 2018;Sugiyama et al, 2018). In this regard, some works were interested in the study of protein structures based on their graph properties and involved the use of topological classifications as in Bartoli et al (2007), where it has been shown that proteins can be considered as small-world networks of amino acids.…”