Efficient Mining of Interesting Patterns in Large Biological Sequences

Pattern discovery in biological sequences (e.g., DNA sequences) is one of the most challenging tasks in computational biology and bioinformatics. So far, in most approaches, the number of occurrences is a major measure of determining whether a pattern is interesting or not. In computational biology, however, a pattern that is not frequent may still be considered very informative if its actual support frequency exceeds the prior expectation by a large margin. In this paper, we propose a new interesting measure that can provide meaningful biological information. We also propose an efficient index-based method for mining such interesting patterns. Experimental results show that our approach can find interesting patterns within an acceptable computation time.

An Efficient Approach to Mining Maximal Contiguous Frequent Patterns from Large DNA Sequence Databases

Mining interesting patterns from DNA sequences is one of the most challenging tasks in bioinformatics and computational biology. Maximal contiguous frequent patterns are preferable for expressing the function and structure of DNA sequences and hence can capture the common data characteristics among related sequences. Biologists are interested in finding frequent orderly arrangements of motifs that are responsible for similar expression of a group of genes. In order to reduce mining time and complexity, however, most existing sequence mining algorithms either focus on finding short DNA sequences or require explicit specification of sequence lengths in advance. The challenge is to find longer sequences without specifying sequence lengths in advance. In this paper, we propose an efficient approach to mining maximal contiguous frequent patterns from large DNA sequence datasets. The experimental results show that our proposed approach is memory-efficient and mines maximal contiguous frequent patterns within a reasonable time.

A XML-based Model for Integration of Individual Health Information / 대한의료정보학회지

OBJECTIVE: For disease prevention and health promotion, it is important that individual health information is managed continuously and can be retrieved easily when necessary. We propose a model for integrated management of diverse individual health information that is generated and managed in heterogeneous Health Information System(HIS)s at hospitals or health centers. METHODS: We use XML and relational database together to represent the health information structurally and manage it flexibly. In order to show the effectiveness of our method, we developed a prototype of Intermediate System for integration and analysed the result. RESULTS: The Intermediate System for integration provides the schema of health information, represents the information in forms of XML strings by the subjects, and stores each XML string as one field of relational database. In each heterogeneous HIS, there is an adaptor that transforms the health information into XML according to the schema. The messages for transmission between each HIS and Intermediate System are represented in XML. CONCLUSION: The experimental study shows that using relational database and XML together provides a flexible, extensible and structured way of representing complex, dynamic, structurally-variant and large-scale information, on the premise that the database tables are partitioned or distributed according to the status of server.