The research of Gene Predicting Algorithms is a key section in bioinformatics. After brief introduction to bioinformatics, we introduce a new and improved Gene Predicting Algorithm, together with a model to give a physical explanation of its meaning....[ Read more ]

The research of Gene Predicting Algorithms is a key section in bioinformatics. After brief introduction to bioinformatics, we introduce a new and improved Gene Predicting Algorithm, together with a model to give a physical explanation of its meaning.

We first use our Improved Entropic Segmentation Algorithm to locate the borders between the Coding and Noncoding regions in DNA sequence. Then, I introduce a method for parameter determination by the continuous condition on random sequence. To give a physical explanation of this algorithm, I built an Ising model of DNA sequence and deduced the exact solution of this model, and as the extension of this model, I gave the exact solution of Potts Model of DNA sequence. To gain more insight into this model, I performed a Monte Carlo simulation of these DNA sequence, and compared the Shannon-Jenson divergence using physical entropy and that using information entropy. The results are very similar. We therefore conclude that they may be used interchangeably. It shows that our model can give a preliminary explanation of this algorithm.

As the other part of my research, I have advanced two algorithms of Point Recognition of DNA microarray figure, with satisfactory results. This is cooperation with Dr. Tam, a professor of biochemistry department of Hong Kong University.