A multiple-pattern biosequence analysis method for diverse source association mining.

Abstract

BACKGROUND: In order to understand the intricacy of biomolecules more comprehensively, significant patterns extracted from related data collected from diverse sources must be integrated. These data sources may be local or distributed, possibly with different representation schemes. Often, related data from different sources correspond only with respect to some of their values. METHODS: In biological sequence analysis, a goal is to identify new, previously unknown, relevant patterns, to obtain additional insights into the biomolecule. This is known as a pattern discovery task, rather than a pattern matching task. In this research, we present a method to tackle this problem typically found in molecular sequence analysis when the alignment of the sequences is represented as a relation. In this article, we propose an information measure to select attribute values that reflect multiple patterns of significant interdependence information. Based on these selected values, the patterns are evaluated with data values from other sources. RESULTS: In the experiments, a cancer-suppressor gene known as TP53 (encoding tumour protein p53) is analysed with the mutation records of patients. The experiments identify previously unknown points in the molecule that have patterns negatively associated with the occurrence of cancer. CONCLUSION: Since the evaluated interdependence pattern is a global property of the molecule, we conjecture that the identified points might also be a reflection of the molecule's cancer-suppressor characteristics. The experiments also confirm the usefulness of the proposed method.