http://www.seipub.org/rbb/RSS.aspxen-US2014-0<p class="abstract">Classification of Time-course Gene Expression Data Using a Hybrid Neural-based Model</p><ul><li>Pages 1-8</li><li>Author Mehdi KhasheiSaeede EftekhariMohammad Reza Hassan Zade</li><li>Abstract The emergence of DNA microarray technologies leads to significant advances in molecular biology. Time-course gene expression data are often measured in order to study dynamic biological systems and gene regulatory networks. It is believed that genes demonstrating similar expression profiles over time might give an informative insight into how underlying biological mechanisms work. This importance leads that in microarray gene research, statistical and intelligent models have received considerable attention to analyze complex time-course gene expression data. Recently, various classification models have been applied in order to find genes which show similar periodic pattern expression. In this paper, we consider the classification of gene expression in temporal expression patterns; then propose an Intelligent hybrid classification model, in which an artificial neural network is combined with a multiple linear regression model to classify genes data. Empirical results show that the proposed model can yield more accurate results than other well-known statistical and intelligent classification models. Therefore, it can be applied as an appropriate approach for classification of gene expression data.</li></ul>http://www.seipub.org/rbb/PaperInfo.aspx?ID=7176Review of Bioinformatics and Biometricshttp://www.seipub.org/rbb/PaperInfo.aspx?ID=71762014-0<p class="abstract">An Extensive Repot on the Efficiency of AIS-INMACA (A Novel Integrated MACA based Clonal Classifier for Protein Coding and Promoter Region Prediction)</p><ul><li>Pages 9-13</li><li>Author Inampudi Ramesh BabuPokkuluri Kiran Sre</li><li>Abstract This paper exclusively reports the efficiency of AIS-INMACA. AIS-INMACA has created good impact on solving major problems in bioinformatics like protein region identification and promoter region prediction with less time (Pokkuluri Kiran Sree, 2014). This AIS-INMACA is now came with several variations (Pokkuluri Kiran Sree, 2014) towards projecting it as a tool in bioinformatics for solving many problems in bioinformatics. So this paper will be very much useful for so many researchers who are working in the domain of bioinformatics with cellular automata.</li></ul>http://www.seipub.org/rbb/PaperInfo.aspx?ID=14487Review of Bioinformatics and Biometricshttp://www.seipub.org/rbb/PaperInfo.aspx?ID=144872014-0<p class="abstract">A New Graphical Representation of DNA Sequences Using Symmetrical Vector Assignment</p><ul><li>Pages 14-21</li><li>Author Satoshi MizutaKyohei Yamaguch</li><li>Abstract Analyzing the similarities between genomic sequences is one of the principal methods used to investigate the evolutionary relationships between species. For relatively short sequences, such as nucleotide sequences of genes or amino acid sequences of proteins, alignment is widely used to evaluate the sequence similarity. However, the alignment is not practical for comparing very long sequences, such as genome sequences, due to its time-consuming nature. In this article, we propose a new method for graphical representation of DNA sequences, which falls into one of the major categories of alignment-free sequence comparison. We introduce a practical method for the numerical conversion of DNA sequences, in which we assign three-dimensional vectors in a symmetrical manner to the bases of genome sequences. We confirm the usefulness of our method in terms of the intuitive assessment of sequence similarities.</li></ul>http://www.seipub.org/rbb/PaperInfo.aspx?ID=15145Review of Bioinformatics and Biometricshttp://www.seipub.org/rbb/PaperInfo.aspx?ID=15145