DNA sequence comparison by a novel probabilistic method

New Method Expression of Protein Sequence and Its Application for Protein Sub-Cellular Localization Prediction

“…Finally, we give the application in prediction of the protein sub-cellular localization based on the new representation and the proposed distance formula (5). In this paper, we collect two data sets of apoptosis proteins, which are often used by many researchers.…”

“…Since then, a variety of graphical representations of DNA sequences have been given by Refs. [2][3][4][5][6]. Xiao 4 and Randic 7 introduced their 2D or 3D graphical representations.…”

Improved Algorithms for Data Visualization in Forensic DNA Analysis

“…Statistical aproach in DNA evidence has been utilized widely in cases of forensic identification over the last few years [5] until this year [6], [7]. The strength of this Improved Algorithms for Data Visualization in Forensic DNA Analysis…”

“…Noor Maizura Mohamad Noor, Senior Member IACSIT, Mohd Iqbal Hakim Harun, and Ahmad Faiz Ghazali evidence against another trial has been used to be presented in court in the form of LR [7]. The ideas and methods for analyzing data and evaluating LRs where Bayesian approach is given special attention [8], [9] and when the evidence is based on STR (short tandem repeat) profiles, data visualization is among the most important part in representation of data for analysis and summary of results.…”

Protein map: An alignment-free sequence comparison method based on various properties of amino acids

“…The recent reviews (Davies et al, 2008;Vinga and Almeida, 2003) on published methods of alignment-free sequence comparison report several concepts of distance measures, such as Markov chain models and Kullback-Leibler discrepancy (Wu et al, 2001), chaos theory (Almeida et al, 2001), Kolmogorov complexity , decision tree induction algorithm (Huang et al, 2004), graphical representation (Liao and Wang, 2004;Randic et al, 2003;Yau et al, 2003), probabilistic measure (Pham and Zuegg, 2004;Yu et al, 2011). Furthermore, sequence vector representation approaches without alignment are also prevalent, such as composition vector (Chan et al, 2010;Chu et al, 2004;Gao and Qi, 2007;Qi et al, 2004), feature vector (Carr et al, 2010;Liu et al, 2006), and moment vector Yu et al, 2010).…”