Fuzzy attributes of a DNA complex: Development of a fuzzy inference engine for codon-“junk” codon delineation

Information-theoretic algorithms in bioinformatics and bio-/medical-imaging: A review

“…Finding the delineating border of separation between codon and noncodon regions in a massive stretch of a DNA chain is a bioinformatic problem. Relevant methodology developed for this purposes uses information-theoretics based metrics so as to score the differentiating extents of statistics between codon-noncodon populations at a given site on the DNA sequence [5]. Hence the concept of statistical divergence, (distance or discriminant is invoked to ascertain the demarcating splicejunctions [5,7].…”

“…Hence a set of three equilateral planes namely, energy (E), entropy (S) and Trends in Information Technology, ICRTIT 2011 MIT, Anna University, Chennai. June [3][4][5]2011 information (H) can be conceived to hold and enclave the hypersphere of informational biology.…”

Fuzzy splicing in precursor-mRNA sequences: prediction of aberrant splice-junctions in viral DNA context

“…Developed in [6] is another strategy that identifies the splice-junctions between codon and non-codon regions present in a massive stretch of a DNA chain, especially when the delineating boundary in question is submerged in a subspace where codon and non-codon parts exist as overlapping and ambiguous/fuzzy entities. A fuzzy inference engine (FIE) developed thereof uses again information-theoretic based metrics (with relevant algorithms applied to symbolic as well as binary sequence data representing the DNA) so as to score differentiating extents of codon/non-codon populations at a given site in the DNA sequence.…”

“…A fuzzy inference engine (FIE) developed thereof uses again information-theoretic based metrics (with relevant algorithms applied to symbolic as well as binary sequence data representing the DNA) so as to score differentiating extents of codon/non-codon populations at a given site in the DNA sequence. The information-theoretic metrics adopted in [6] refer to various statistical divergence (such as KL and JS measures) as well as distance and discriminant concepts. Further, the algorithms indicated in [6] yield consistent results on the delineation boundary sought on test subspaces that are fuzzy; and simulated studies using human as well as bacteria codonstatistics confirm the efficacy of the approach pursued.…”

“…The information-theoretic metrics adopted in [6] refer to various statistical divergence (such as KL and JS measures) as well as distance and discriminant concepts. Further, the algorithms indicated in [6] yield consistent results on the delineation boundary sought on test subspaces that are fuzzy; and simulated studies using human as well as bacteria codonstatistics confirm the efficacy of the approach pursued.…”