Modification of Support Vector Machine for Microarray Data Analysis

The role of protuberant data analysis in selection of certain genes having distinctive level of activities between conditions of interest i.e diseased gene and normal genes is very significant. Nowa- days it is become a standard in gene analysis that microarray of DNA is a crucial data preparation step in systemization and other biological analysis. We consider the problem of constructing an accurate prediction rule for separating the different labels of genes in microarray gene expression data. Use of SVM in such data analysis is not new but it is not up to the mark we desire. So in this manuscript, we have tried to modify Support Vector Machine (SVM) for better accuracy in cancer genes systemization. Here we have modified SVM to account for gene redundancy and keep a check on it. In the other approach, instead of keeping bias a constant in SVM, we have tried to modify SVM by bias variation which we call as Orthogonal Vertical Permutator (OVP)

Quadruple context-free L-System mathematical tools as origin of biological evolution

It is well known that A, T, G, C annealed together early in evolution and the long stretch of DNA was found which ultimately resulted into chromosomes of different organisms. But it is unclear till date how exons, introns, conserved protein domains was formed. Using the DNA sequences of the largest known gene-family present in human genome, i.e., olfactory receptors and simplest possible quadruple context-free L-Systems, we show that conserved protein domains and intergenic regions which lies at the heart of the biological evolution started with a sixteen base-pairs stretch of DNA

DNA Sequence Evolution through Integral Value Transformations

In deciphering the DNA structures, evolutions and functions, Cellular Automata (CA) do have a significant role. DNA can be thought of as a one-dimensional multi-state CA, more precisely four states of CA namely A, T, C, and G which can be taken as numerals 0, 1, 2 and 3. Earlier, G.Ch. Sirakoulis et al reported the DNA structure, evolution and function through quaternary logic one dimensional CA and the authors have found the simulation results of DNA evolutions with the help of only four linear CA rules. The DNA sequences which are produced through the CA evolutions, however, are seen by our research team not to exist in the established databases of various genomes although the initial seed (initial global state of CA) was taken from the database. This problem motivated us to study the DNA evolutions from a more fundamental point of view. Parallel to the CA paradigm we have devised an enriched set of discrete transformations which have been named as Integral Value Transformations (IVT). Interestingly, on applying the IVT systematically, we have been able to show that each of the DNA sequences at various discrete time instances in IVT evolutions can be directly mapped to a specific DNA sequence existing in the database. This has been possible through our efforts of getting quantitative mathematical parameters of the DNA sequences involving Fractals. Thus we have at our disposal some transformational mechanism between one DNA to another

Underlying Mathematics in Diversification of Human Olfactory Receptors in Different Loci

As per conservative estimate, approximately (51-105) Olfactory Receptors (ORs) loci are present in human genome occurring in clusters. These clusters are apparently unevenly spread as mosaics over 21 pair of human chromosomes. Olfactory Receptor (OR) gene families which are thought to have expanded for the need to provide recognition capability for huge number of pure and complex odorants. ORs form the largest known multi-gene family in the human genome. Recent studies have shown that 388 full length and 414 OR pseudo-genes are present in these OR genomic clusters. In this paper, the authors report a classification method for all human ORs based on their sequential quantitative information like presence of poly strings of nucleotides bases, long range correlation and so on. An L-System generated sequence has been taken as an input into a star-model of specific subfamily members and resultant sequence has been mapped to a specific OR based on the classification scheme using fractal parameters like Hurst exponent and fractal dimensions

A Quantitative Model for Human Olfactory Receptors

A wide variety of chemicals having distinct odors are smelled by humans. Odor perception initiates in the nose, where it is detected by a large family of olfactory receptors (ORs). Based on divergence of evolutionary model, a sequence of human ORs database has been proposed by D. Lancet et al (2000, 2006). It is quite impossible to infer whether a given sequence of nucleotides is a human OR or not, without any biological experimental validation. In our perspective, a proper quantitative understanding of these ORs is required to justify or nullify whether a given sequence is a human OR or not. In this paper, all human OR sequences have been quantified, and a set of clusters have been made using the quantitative results based on two different metrics. Using this proposed quantitative model, one can easily make probable justification or deterministic nullification whether a given sequence of nucleotides is a probable human OR homologue or not, without seeking any biological experiment. Of course a further biological experiment is essential to validate the probable human OR homologue