Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Faster genome annotation of non-coding RNA families without loss of accuracy
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
DDR: an index method for large time-series datasets
Information Systems
Identifying bridging rules between conceptual clusters
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Sharing and access right delegation for confidential documents: a practical solution
Information and Management
Deciding the financial health of dot-coms using rough sets
Information and Management
Detecting inconsistency in biological molecular databases using ontologies
Data Mining and Knowledge Discovery
Brief Communication: Finding rule groups to classify high dimensional gene expression datasets
Computational Biology and Chemistry
Discovery of Structural and Functional Features in RNA Pseudoknots
IEEE Transactions on Knowledge and Data Engineering
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ncRNAs play an important role in the regulation of gene expression. However, many of their functions have not yet been fully discovered. There are complicated relationships between ncRNAs in different categories. Finding these relationships can contribute to identify ncRNAs' functions and properties. We extend the association rule to represent the relationship between two ncRNAs. Based on this rule, we can speculate the ncRNA's function when it interacts with other ncRNAs. We propose two measures to explore the relationships between ncRNAs in different categories. Entropy theory is to calculate how close two ncRNAs are. Association rule is to represent the interactions between ncRNAs. We use three datasets from miRBase and RNAdb. Two from miRBase are designed for finding relationships between miRNAs; the other from RNAdb is designed for relationships among miRNA, snoRNA and piRNA. We evaluate our measures from both biological significance and performance perspectives. All the cross-species patterns regarding miRNA that we found are proven correct using miRNAMap 2.0. In addition, we find novel cross-genomes patterns such as (hsa-mir-190b-hsa-mir-153-2). According to the patterns we find, we can (1) explore one ncRNA's function from another with known function and (2) speculate the functions of both of them based on the relationship even we do no understand either of them. Our methods' merits also include: (1) they are suitable for any ncRNA datasets and (2) they are not sensitive to the parameters.