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Machine learning for the identification of the DNA variations for diseases diagnosis

Martyna J.

Studia Informatica

2011

Vol. 32, nr 3B

103-118

In this paper we give an overview of a basic computational haplotype analysis, including the pairwaise association with the use of clustering, and tagged prediction (using Bayesian networks). Moreover, we present several machine learning methods in order to explore the association between human genetic variations and diseases. These methods include the clustering of SNPs based on some similarity measures and selecting of one SNP per cluster, the support vector machines, etc. The presented machine learning methods can help to generate a plausible hypothesis for some classification systems.

W pracy przedstawiono podstawowe metody uczenia maszynowego dla wyboru haplotypów, m.in. asocjacji par z użyciem klastrowania i przewidywania, znaczonego SNP (Single Nucleotide Polimorhisms), maszyny wektorów wspierających (ang. Support Vector Machines, SVM) itp. Metody te znajdują zastosowanie w przewidywaniu chorób. Mogą być także pomocne do generowania prawdopodobnych hipotez dla systemów klasyfikacji chorób.

The EM Algorithm and Its Implementation for the Estimation of Frequencies of snp-Haplotypes

Polańska J.

International Journal of Applied Mathematics and Computer Science

2003

Vol. 13, no 3

419-429

A haplotype analysis is becoming increasingly important in studying complex genetic diseases. Various algorithms and specialized computer software have been developed to statistically estimate haplotype frequencies from marker phenotypes in unrelated individuals. However, currently there are very few empirical reports on the performance of the methods for the recovery of haplotype frequencies. One of the most widely used methods of haplotype reconstruction is the Maximum Likelihood method, employing the Expectation-Maximization (EM) algorithm. The aim of this study is to explore the variability of the EM estimates of the haplotype frequency for real data. We analyzed haplotypes at the BLM, WRN, RECQL and ATM genes with 8-14 biallelic markers per gene in 300 individuals. We also re-analyzed the data presented by Mano et al. (2002). We studied the convergence speed, the shape of the loglikelihood hypersurface, and the existence of local maxima, as well as their relations with heterozygosity, the linkage disequilibrium and departures from the Hardy-Weinberg equilibrium. Our study contributes to determining practical values for algorithm sensitivities.