Exploring Consensus RNA Substructural Patterns Using Subgraph Mining

Qingfeng Chen

doi:10.1109/TCBB.2016.2645202

Exploring Consensus RNA Substructural Patterns Using Subgraph Mining

2017-09-01

11

PRE

AI

DOI：10.1109/TCBB.2016.2645202

原文链接

原文求助

摘要

En 中文

Frequently recurring RNA structural motifs play important roles in RNA folding process and interaction with other molecules. Traditional index-based and shape-based schemas are useful in modeling RNA secondary structures but ignore the structural discrepancy of individual RNA family member. Further, the in-depth analysis of underlying substructure pattern is insufficient due to varied and unnormalized substructure data. This prevents us from understanding RNAs functions and their inherent synergistic regulation networks. This article thus proposes a novel labeled graph-based algorithm RnaGraph to uncover frequently RNA substructure patterns. Attribute data and graph data are combined to characterize diverse substructures and their correlations, respectively. Further, a top-k graph pattern mining algorithm is developed to extract interesting substructure motifs by integrating frequency and similarity. The experimental results show that our methods assist in not only modelling complex RNA secondary structures but also identifying hidden but interesting RNA substructure patterns.

Keyword:

Data mining

RNA

subgraph

substructure

support

Exploring Consensus RNA Substructural Patterns Using Subgraph Mining

摘要

期刊

机构