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Artykuł dostępny w postaci pełnego tekstu - kliknij by otworzyć plik
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Bayesian birds: concerning the paper of R. F. Green

100%
Kimmel M.
Mathematica Applicanda
|
1983
|
tom 11
|
nr 22
PL
Artykuł nie zawiera streszczenia
EN
The article contains no abstract
2
Content available remote

Stochastic models of progression of cancer andtheir use in controlling cancer-related mortality

63%
Kimmel M., Gorlova O. Y.
International Journal of Applied Mathematics and Computer Science
|
2003
|
tom 13
|
nr 3
279-287
EN
A construction of a realistic statistical model of lung cancer risk and progression is proposed. The essential elements of the model are genetic and behavioral determinants of susceptibility, progression of the disease from precursor lesions through early (localized) tumors to disseminated disease, detection by various modalities, and medical intervention. Using model estimates as a foundation, mortality reduction caused by early-detection and intervention programs can be predicted under different scenarios. Genetic indicators of susceptibility to lung cancer should be used to define the highest-risk subgroups of the high-risk behavior population (smokers). The calibration and validation of the model requires applying our techniques to a variety of data sets available, including public registry data of the SEER type, data from the NCI lung cancer chest X-ray screening studies, and the recent ELCAP CT-scan screening study.
3
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Population genetics models for the statistics of DNA samples under different demographic scenarios - Maximum likelihood versus approximate methods

63%
Polański A., Kimmel M.
International Journal of Applied Mathematics and Computer Science
|
2003
|
tom 13
|
nr 3
347-355
EN
The paper reviews the basic mathematical methodology of modeling neutral genetic evolution, including the statistics of the Fisher-Wright process, models of mutation and the coalescence method under various demographic scenarios. The basic approach is the use of maximum likelihood techniques. However, due to computational problems, intuitive or approximate methods are also of great importance.
4
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Sampling properties of estimators of nucleotide diversity at discovered SNP sites

39%
Renwick A., Bonnen P. E., Trikka D., Nelson D. L., Chakraborty R., Kimmel M.
International Journal of Applied Mathematics and Computer Science
|
2003
|
tom 13
|
nr 3
385-394
EN
SNP sites are generally discovered by sequencing regions of the human genome in a limited number of individuals. This may leave SNP sites present in the region, but containing rare mutant nucleotides, undetected. Consequently, estimates of nucleotide diversity obtained from assays of detected SNP sites are biased. In this research we present a statistical model of the SNP discovery process, which is used to evaluate the extent of this bias. This model involves the symmetric Beta distribution of variant frequencies at SNP sites, with an additional probability that there is no SNP at any given site. Under this model of allele frequency distributions at SNP sites, we show that nucleotide diversity is always underestimated. However, the extent of bias does not seem to exceed 10-15% for the analyzed data. We find that our model of allele frequency distributions at SNP sites is consistent with SNP statistics derived based on new SNP data at ATM, BLM, RQL and WRN gene regions. The application of the theory to these new SNP data as well as to the literature data at the LPL gene region indicates that in spite of ascertainment biases, the observed differences of nucleotide diversity across these gene regions are real. This provides interesting evidence concerning the heterogeneity of the rates of nucleotide substitution across the genome.
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