Studies on information-theoretics based data-sequence pattern-discriminant algorithms: Applications in bioinformatic data mining.

摘要

This research refers to studies on information-theoretic (IT) aspects of data-sequence patterns and developing thereof discriminant algorithms that enable distinguishing the features of underlying sequence patterns having characteristic, inherent stochastical attributes. The application potentials of such algorithms include bioinformatic data mining efforts.; Consistent with the scope of the study as above, considered in this research are specific details on information-theoretics and entropy considerations vis-á-vis sequence patterns (having stochastical attributes) such as DNA sequences of molecular biology. Applying information-theoretic concepts (essentially in Shannon's sense), the following distinct sets of metrics are developed and applied in the algorithms developed for data-sequence pattern-discrimination applications: (i) Divergence or cross-entropy algorithms of Kullback-Leibler type and of general Czizár class; (ii) statistical distance measures; (iii) ratio-metrics; (iv) Fisher type linear-discriminant measure and (v) complexity metric based on information redundancy.; These measures are judiciously adopted in ascertaining codon-noncodon delineations in DNA sequences that consist of crisp and/or fuzzy nucleotide domains across their chains. The Fisher measure is also used in codon-noncodon delineation and in motif detection. Relevant algorithms are used to test DNA sequences of human and some bacterial organisms. The relative efficacy of the metrics and the algorithms is determined and discussed. The potentials of such algorithms in supplementing the prevailing methods are indicated. Scope for future studies is identified in terms of persisting open questions.