Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Nikita A Sakhanenko"'
Publikováno v:
BMC Bioinformatics, Vol 22, Iss 1, Pp 1-11 (2021)
Abstract Background Permutation testing is often considered the “gold standard” for multi-test significance analysis, as it is an exact test requiring few assumptions about the distribution being computed. However, it can be computationally very
Externí odkaz:
https://doaj.org/article/3a77b291e81c46bcac08e9557dd8db84
Autor:
Theresa A. Lusardi, Ursula S. Sandau, Nikita A. Sakhanenko, Sarah Catherine B. Baker, Jack T. Wiedrick, Jodi A. Lapidus, Murray A. Raskind, Ge Li, Elaine R. Peskind, David J. Galas, Joseph F. Quinn, Julie A. Saugstad
Publikováno v:
Frontiers in Neuroscience, Vol 15 (2021)
A history of traumatic brain injury (TBI) increases the odds of developing Alzheimer’s disease (AD). The long latent period between injury and dementia makes it difficult to study molecular changes initiated by TBI that may increase the risk of dev
Externí odkaz:
https://doaj.org/article/04934a033db94acca68861ee751f4e26
Publikováno v:
G3: Genes, Genomes, Genetics, Vol 9, Iss 7, Pp 2071-2088 (2019)
We describe an information-theory-based method and associated software for computationally identifying sister spores derived from the same meiotic tetrad. The method exploits specific DNA sequence features of tetrads that result from meiotic centrome
Externí odkaz:
https://doaj.org/article/d7624589b0994064baef2e63b4daddf3
Publikováno v:
Journal of Computational Biology. 30:323-336
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::627182b8eb38ebf67bc60e2e018c8e66
https://doi.org/10.1089/cmb.2022.0185
https://doi.org/10.1089/cmb.2022.0185
Autor:
Lisa Uechi, Mahjoubeh Jalali, Jayson D Wilbur, Jonathan L French, N L Jumbe, Michael J Meaney, Peter D Gluckman, Neerja Karnani, Nikita A Sakhanenko, David J Galas, GUSTO study group
Publikováno v:
PLoS ONE, Vol 15, Iss 12, p e0242684 (2020)
The genetic mechanisms of childhood development in its many facets remain largely undeciphered. In the population of healthy infants studied in the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) program, we have identified a range of depend
Externí odkaz:
https://doaj.org/article/010698ddab7d40bfbe98976b97f6b708
Publikováno v:
Journal of Computational Biology
Quantitative genetics has evolved dramatically in the past century, and the proliferation of genetic data, in quantity as well as type, enables the characterization of complex interactions and mechanisms beyond the scope of its theoretical foundation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::905b9bc5e0bd0176430c6d8657e25326
https://doi.org/10.1089/cmb.2020.0032
https://doi.org/10.1089/cmb.2020.0032
Autor:
David J. Galas, Nikita A. Sakhanenko
Publikováno v:
Entropy, Vol 21, Iss 1, p 88 (2019)
Relations between common information measures include the duality relations based on Möbius inversion on lattices, which are the direct consequence of the symmetries of the lattices of the sets of variables (subsets ordered by inclusion). In this pa
Externí odkaz:
https://doaj.org/article/6176b8efb1304e6ba68c58c77fa976ab
Publikováno v:
Proceedings of Entropy 2021: The Scientific Tool of the 21st Century.
Developing an information theory of quantitative genetics David J. Galas, James Kunert-Graf and Nikita A. Sakhanenko Pacific Northwest Research Institute, Seattle Washington, 98122 USA Quantitative genetics has evolved dramatically in the century sin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::307e662108158c8ca1a3a14ee9333632
https://doi.org/10.3390/entropy2021-09821
https://doi.org/10.3390/entropy2021-09821
Publikováno v:
Journal of Computational Biology
Missing values in complex biological data sets have significant impacts on our ability to correctly detect and quantify interactions in biological systems and to infer relationships accurately. In this article, we propose a useful metaphor to show th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::73968cd1c710bccd9110dbca17b30426
https://doi.org/10.1089/cmb.2018.0179
https://doi.org/10.1089/cmb.2018.0179
Publikováno v:
Axioms, Vol 6, Iss 2, p 8 (2017)
Inferring and comparing complex, multivariable probability density functions is fundamental to problems in several fields, including probabilistic learning, network theory, and data analysis. Classification and prediction are the two faces of this cl
Externí odkaz:
https://doaj.org/article/53cff497cc5440bc868ff5c7e120f21d