Description

ABSTRACT

This study explains the way of considering purine and pyrimidine residues (regarded as biomarkers) existing across splice-junctions in human genomic sequences; and, any associated artifact details observed thereof are assessed (via compatible statistical divergence metrics) so as to diagnose any prevailing pathogenic state (like  cancer). That is, considering a domain of purine and pyrimidine infestations as above (identified as biomarkers), it is marked as a sample-space, X with no mutations on residues; otherwise, when the residues are mutated  (posing artifact details in the biomarkers), relavant sample-space is identified as Y.  The statistical divergence between X and Y is proposed here as a possible measure of pathogenic condition due to the presence of mutations (manifesting as artefacts in the biomarkers of Y). That is, the residues of Y depict a set of randomly comingled items of artefacts due to mutational changes. Hence, the statistical ensembles of artificial sample-spaces, X and Y simulated denote a pair of biomarker sets with a finite statistical divergence as a result of their common and/or differential signatures. The said statistical divergence is determined using a compatible (cross-entropy) measure. It is expressed in terms of the ratio of purine and pyrimidine residues (rP-P) parameter. In summary, the underlying efforts denote in silico simulations emulating real-world sample-spaces of X and Y; and,  the computed results on statistical distance are interpreted as  possible indications of pathogenic conditions  due to the presence of mutations.

Keywords: Biomarkers, purine-pyramidine ratio, splice-junctions, cross-entropy, divergence measure, mutual information, cancer diagnostic metrics, in silico simulations