Alanine aminotransferase (ALT) is an enzymatic protein involved in catabolism of amino acids. The carbon distribution study on this clinically important protein is carried out here. The study reveals that the carbon content is generally higher than the expected values of 31.45%. The alteration in carbon content other than the active site might improve the activity of this enzymatic protein. Particularly the reduction at the carboxyl end of the sequence is more appropriate. Carbon distribution analysis clearly locates the active site of ALT protein, which is reported here.

MicroRNAs (miRNA’s) constitute a large family of non coding RNAs that function to regulate gene expression. Wet lab experiments usually used to classify the miRNA of plants and animals are highly expensive, labor intensive and time consuming. Thus there arises a need for computational approach for classification of plant and animal miRNA. These computational approaches are fast and economical as compared to wet lab techniques. Here a machine learning approach is used to classify miRNA of HIV, plants and animals. The new SVM learning algorithm called Weka LibSVM has been used for classification of plant and animal and HIVmiRNA. The model has been tested on available data and it gives results with 95% accuracy.

DNA microarray technology is a fundamental tool in gene expression data analysis. The collection of datasets from the technology has underscored the need for quantitative analytical tools to examine such data. Due to the large number of genes and complex gene regulation networks, clustering is a useful exploratory technique for analyzing these data. Many clustering algorithms have been proposed to analyze microarray gene expression data, but very few of them evaluate the quality of the clusters. In this paper, a novel cluster analysis technique has been proposed without considering number of clusters a priori. The method computes a similarity measurement function based on which the clusters are merged and subsequently splits a cluster by computing the degree of separation of the cluster. The process of splitting and merging performs iteratively until the cluster validity index (i.e. DB index) degrades. The experimental result shows that the proposed cluster analysis technique gives comparable results on gene cancer dataset with existing methods. This study may help raise relevant issues in the extraction of meaningful biological information from microarray expression data.

Gene Ontology (GO) is a controlled vocabulary to describe the gene function. Each GO term is constructed by a hierarchical structure of gene function, so it is suitable for describing relationships of gene functions. We applied Bayesian network model as a training model using GO terms to identify unknown gene functions, and used Bayesian network model with three different heterogeneous data sets and multi-layered classifier to automatically predict gene functions. This proposed model is comprised of a base-classifier and a meta-classifier. The base-classifier serves a base of meta-classifier with Bayesian network model and meta-classifier plays role of classifying the designated GO term from the root node. A comparative analysis of our suggested model and other gene functional annotation systems shows that our model outperforms than others especially in terms of a number of correctly predicted proteins.

Bioinformatics is an interdisciplinary area of the science composed of biology, mathematics and computer science. Bioinformatics is the application of information technology to manage biological data that helps in decoding plant genomes. During the last two decades enormous data has been generated in biological science, firstly, with the onset of sequencing the genomes of model organisms and, secondly, rapid application of high throughput experimental techniques in laboratory research. Biological research that earlier used to start in laboratories, fields and plant clinics is now starts at the computational level using computers (In-silico) for analysis of the data, experiment planning and hypothesis development. Bioinformatics develops algorithms and suitable data analysis tools to infer the information and make discoveries. Application of various bioinformatics tools in biological research enables storage, retrieval, analysis, annotation and visualization of results and promotes better understanding of biological system in fullness. This will help in plant health care based disease diagnosis to improve the quality of Plant.

In recent years there has been much focus on the use of single nucleotide polymorphism (SNP) fine genome mapping to identify causative mutations for traits of interest; however, many studies focus only on the marginal effects of markers, ignoring potential gene interactions. Simulation studies have shown that this approach may not be powerful enough to detect important loci when gene interactions are present. Although several attempts have been made to study potential gene interaction, the number of SNP markers considered in these studies is often limited. Given the prohibitive computation cost of modeling interactions in studies involving a large number SNP, there is a need for methods that can account for potential gene interactions in a computationally efficient manner to be developed. In this study, the ant colony optimization algorithm (ACA) and logistic regression on large number of SNP genotypes were used. Our procedure was compared to sliding window (SW/H), and single locus genotype association (RG) methods used in haplotype analyses. A binary trait simulated using an epistatic model and HapMap ENCODE SNP genotypes was used to evaluate each algorithm. Results show that the ACA outperformed SW/H and RG under several simulation scenarios, yielding substantial increases in power to detect genomic regions associated with the simulated trait.

The purpose of this study was to investigate the effects of three basic cyclic rhythms called biorhythms including a 23-day physical cycle, a 28-day emotional cycle and a 33-day intellectual cycle on the incidence of accidents in Agra casting manufacturing units. For this purpose a sample of 462 accidents were randomly selected for the analysis. The statistical methods chi-square of significance level p<0.05and 95%CI were used to determine the significance of the study. Results of the study showed that Chi-square values were significant at p<0.05 in all the cases except in the case of serious accidents as response of results were mixed but the value of 95%CI is significant in all the cases as the values were much more than expected to occur on critical days purely by chance 20.4%. It was found, using alternative definition 4 of the critical days that 67.2% of accidents in case of total number of accidents examined and 72.8% of accidents in case of serious accidents were occurring on critical days for the various combinations of alternative critical days definition, which were higher than the other alternative definitions of critical days. Present work suggested that biorhythm theory acts as an information system and may be implemented in the industries in which chances of workers to get stuck with accidents are more. Relevance to industry- Some industrial activities involve lifting and carrying heavy loads or working in dusty, fummy, and heated environment. Such tasks may lead to various types of accidents to the workers. This study investigated the effects of biorhythm on industrial accidents, so that the workers could be kept away from the hazardous tasks on critical days. The results may provide useful information for further study in the prevention of industrial accidents.