Изучение качества классификации типов тканей по экспрессии различного числа генов
This proceedings publication is a compilation of selected contributions from the “Third International Conference on the Dynamics of Information Systems” which took place at the University of Florida, Gainesville, February 16–18, 2011. The purpose of this conference was to bring together scientists and engineers from industry, government, and academia in order to exchange new discoveries and results in a broad range of topics relevant to the theory and practice of dynamics of information systems. Dynamics of Information Systems: Mathematical Foundation presents state-of-the art research and is intended for graduate students and researchers interested in some of the most recent discoveries in information theory and dynamical systems. Scientists in other disciplines may also benefit from the applications of new developments to their own area of study.
We studied the expression of peroxiredoxin genes (PRDX1, PRDX2, PRDX3, and PRDX6) in human erythroleukemia K652, human breast carcinoma MCF-7, and human ovarian carcinoma SKOV-3 cells during cisplatin resistance development. It was found that drug resistance formation was accompanied by a significant increase in the expression of PRDX1, PRDX2, PRDX3, PRDX6 genes in all cancer cell strains, which confirms the important contribution of redox-dependent mechanisms into the development of cisplatin resistance of cancer cells.
At the moment, for the equalization of reads histogram, which derived from the treatment of the transcriptome of diﬀerent individuals, it is suggested to use a negative binomial distribution. In this paper we analyze the “physical” basis of a broadening of Poisson distribution, and conclude that the true form of the distribution is really compound Poisson distribution (a special case of which is the negative binomial distribution), but the true choice is another special case of this distribution, i.e. n-times convolution (n is a random variable with Poisson distribution) of random variables with the exponential (not logarithmical) distribution. It is shown that a distribution of gene expression intensity in a group of individuals calculated from the published data is described better by the convolution of exponential functions.
A form for an unbiased estimate of the coefficient of determination of a linear regression model is obtained. It is calculated by using a sample from a multivariate normal distribution. This estimate is proposed as an alternative criterion for a choice of regression factors.