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Your search keyword '"Sakhanenko, Nikita A."' showing total 26 results
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26 results on '"Sakhanenko, Nikita A."'

1. Expansion of the Kullback-Leibler Divergence, and a new class of information metrics

Author

Galas, David J., Dewey, T. Gregory, Kunert-Graf, James, and Sakhanenko, Nikita A.

Subjects
Computer Science - Information Theory, Quantitative Biology - Quantitative Methods
Abstract

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 class of problem. We take an approach here that simplifies many aspects of these problems by presenting a structured, series expansion of the Kullback-Leibler divergence - a function central to information theory - and devise a distance metric based on this divergence. Using the M\"obius inversion duality between multivariable entropies and multivariable interaction information, we express the divergence as an additive series in the number of interacting variables, which provides a restricted and simplified set of distributions to use as approximation and with which to model data. Truncations of this series yield approximations based on the number of interacting variables. The first few terms of the expansion-truncation are illustrated and shown to lead naturally to familiar approximations, including the well-known Kirkwood superposition approximation. Truncation can also induce a simple relation between the multi-information and the interaction information. A measure of distance between distributions, based on Kullback-Leibler divergence, is then described and shown to be a true metric if properly restricted. The expansion is shown to generate a hierarchy of metrics and connects this work to information geometry formalisms. We give an example of the application of these metrics to a graph comparision problem that shows that the formalism can be applied to a wide range of network problems, provides a general approach for systematic approximations in numbers of interactions or connections, and a related quantitative metric., Comment: 25 pages, 1 figure

Published
2017

2. Multivariate information measures: a unification using M\'obius operators on subset lattices

Author

Galas, David J. and Sakhanenko, Nikita A.

Subjects
Computer Science - Information Theory, Quantitative Biology - Other Quantitative Biology
Abstract

Information related measures are useful tools for multi variable data analysis, as measures of dependence among variables, and as descriptions of order in biological and physical systems. Information related measures, like marginal entropies, mutual / interaction / multi-information, have been used in a number of fields including descriptions of systems complexity and biological data analysis. The mathematical relationships among these measures are therefore of significant interest. Relations between common information measures include the duality relations based on M\"obius inversion on lattices. These are the direct consequence of the symmetries of the lattices of the sets of variables (subsets ordered by inclusion). While the mathematical properties and relationships among these information-related measures are of significant interest, there has been, to our knowledge, no systematic examination of the full range of relationships and no unification of this diverse range of functions into a single formalism as we do here. In this paper we define operators on functions on these lattices based on the M\"obius inversion idea that map the functions into one another (M\"obius operators.) We show that these operators form a simple group isomorphic to the symmetric group S3. Relations among the set of functions on the lattice are transparently expressed in terms of the operator algebra, and, applied to the information measures, can be used to derive a wide range of relationships among measures. We describe a direct relation between sums of conditional log-likelihoods and previously defined dependency measures. The algebra is naturally generalized which yields more extensive relationships. This formalism provides a fundamental unification of information related measures, but isomorphism of all distributive lattices with the subset lattice implies broad potential application of these results., Comment: 26 pages, 6 figures, 2 tabels

Published
2016

5. Cerebrospinal Fluid MicroRNA Changes in Cognitively Normal Veterans With a History of Deployment-Associated Mild Traumatic Brain Injury

Author

Lusardi, Theresa A., primary, Sandau, Ursula S., additional, Sakhanenko, Nikita A., additional, Baker, Sarah Catherine B., additional, Wiedrick, Jack T., additional, Lapidus, Jodi A., additional, Raskind, Murray A., additional, Li, Ge, additional, Peskind, Elaine R., additional, Galas, David J., additional, Quinn, Joseph F., additional, and Saugstad, Julie A., additional

Published
2021
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6. Children’s erythrocyte fatty acids are associated with the risk of islet autoimmunity

Author

Niinistö, Sari, Erlund, Iris, Lee, Hye Seung, Uusitalo, Ulla, Salminen, Irma, Aronsson, Carin Andrén, Parikh, Hemang M., Liu, Xiang, Hummel, Sandra, Toppari, Jorma, She, Jin Xiong, Lernmark, Åke, Ziegler, Annette G., Rewers, Marian, Akolkar, Beena, Krischer, Jeffrey P., Galas, David, Das, Siba, Sakhanenko, Nikita, Rich, Stephen S., Hagopian, William, Norris, Jill M., Virtanen, Suvi M., The TEDDY Study Group., Tampere University, Health Sciences, and Department of Paediatrics

Subjects
Male, 0301 basic medicine, Erythrocytes, Conjugated linoleic acid, The Environmental Determinants of Diabetes in the Young, Autoimmunity, Pathogenesis, chemistry.chemical_compound, Endocrinology, 0302 clinical medicine, 3123 Gynaecology and paediatrics, Child, chemistry.chemical_classification, Multidisciplinary, geography.geographical_feature_category, Fatty Acids, Islet, Breast Feeding, Child, Preschool, Medicine, Female, Docosapentaenoic acid, Polyunsaturated fatty acid, medicine.medical_specialty, Science, 030209 endocrinology & metabolism, Lower risk, Article, Islets of Langerhans, 03 medical and health sciences, Internal medicine, medicine, Humans, geography, Type 1 diabetes, business.industry, Infant, Fatty acid, medicine.disease, 3141 Health care science, 030104 developmental biology, Risk factors, chemistry, Case-Control Studies, 3111 Biomedicine, business, Biomarkers
Abstract

Our aim was to investigate the associations between erythrocyte fatty acids and the risk of islet autoimmunity in children. The Environmental Determinants of Diabetes in the Young Study (TEDDY) is a longitudinal cohort study of children at high genetic risk for type 1 diabetes (n = 8676) born between 2004 and 2010 in the U.S., Finland, Sweden, and Germany. A nested case–control design comprised 398 cases with islet autoimmunity and 1178 sero-negative controls matched for clinical site, family history, and gender. Fatty acids composition was measured in erythrocytes collected at the age of 3, 6, and 12 months and then annually up to 6 years of age. Conditional logistic regression models were adjusted for HLA risk genotype, ancestry, and weight z-score. Higher eicosapentaenoic and docosapentaenoic acid (n − 3 polyunsaturated fatty acids) levels during infancy and conjugated linoleic acid after infancy were associated with a lower risk of islet autoimmunity. Furthermore, higher levels of some even-chain saturated (SFA) and monounsaturated fatty acids (MUFA) were associated with increased risk. Fatty acid status in early life may signal the risk for islet autoimmunity, especially n − 3 fatty acids may be protective, while increased levels of some SFAs and MUFAs may precede islet autoimmunity.

Published
2021

13. The Extracellular RNA Communication Consortium: Establishing Foundational Knowledge and Technologies for Extracellular RNA Research

Author

Das, Saumya, primary, Ansel, K. Mark, additional, Bitzer, Markus, additional, Breakefield, Xandra O., additional, Charest, Alain, additional, Galas, David J., additional, Gerstein, Mark B., additional, Gupta, Mihir, additional, Milosavljevic, Aleksandar, additional, McManus, Michael T., additional, Patel, Tushar, additional, Raffai, Robert L., additional, Rozowsky, Joel, additional, Roth, Matthew E., additional, Saugstad, Julie A., additional, Van Keuren-Jensen, Kendall, additional, Weaver, Alissa M., additional, Laurent, Louise C., additional, Abdel-Mageed, Asim B., additional, Adamidi, Catherine, additional, Adelson, P. David, additional, Akat, Kemal M., additional, Alsop, Eric, additional, Arango, Jorge, additional, Aronin, Neil, additional, Avsaroglu, Seda Kilinc, additional, Azizian, Azadeh, additional, Balaj, Leonora, additional, Ben-Dov, Iddo Z., additional, Bertram, Karl, additional, Blelloch, Robert, additional, Bogardus, Kimberly A., additional, Breakefield, Xandra Owens, additional, Calin, George A., additional, Carter, Bob S., additional, Charest, Al, additional, Chen, Clark C., additional, Chitnis, Tanuja, additional, Coffey, Robert J., additional, Courtright-Lim, Amanda, additional, Das, Saumya, additional, Datta, Amrita, additional, DeHoff, Peter, additional, Diacovo, Thomas G., additional, Erle, David J., additional, Etheridge, Alton, additional, Ferrer, Marc, additional, Franklin, Jeffrey L., additional, Freedman, Jane E., additional, Galeev, Timur, additional, Gandhi, Roopali, additional, Garcia, Aitor, additional, Gerstein, Mark Bender, additional, Ghai, Vikas, additional, Ghiran, Ionita Calin, additional, Giraldez, Maria D., additional, Goga, Andrei, additional, Gogakos, Tasos, additional, Goilav, Beatrice, additional, Gould, Stephen J., additional, Guo, Peixuan, additional, Hochberg, Fred, additional, Huang, Bo, additional, Huentelman, Matt, additional, Hunter, Craig, additional, Hutchins, Elizabeth, additional, Jackson, Andrew R., additional, Kalani, M. Yashar S., additional, Kanlikilicer, Pinar, additional, Karaszti, Reka Agnes, additional, Khvorova, Anastasia, additional, Kim, Yong, additional, Kim, Hogyoung, additional, Kim, Taek Kyun, additional, Kitchen, Robert, additional, Kraig, Richard P., additional, Krichevsky, Anna M., additional, Kwong, Raymond Y., additional, Lee, Minyoung, additional, L’Etoile, Noelle, additional, Levy, Shawn E., additional, Li, Feng, additional, Li, Jenny, additional, Li, Xin, additional, Lopez-Berestein, Gabriel, additional, Lucero, Rocco, additional, Mateescu, Bogdan, additional, Matin, A.C., additional, Max, Klaas E.A., additional, Mempel, Thorsten R., additional, Meyer, Cindy, additional, Mondal, Debasis, additional, Mukamal, Kenneth Jay, additional, Murillo, Oscar D., additional, Muthukumar, Thangamani, additional, Nickerson, Deborah A., additional, O’Donnell, Christopher J., additional, Patel, Dinshaw J., additional, Patton, James G., additional, Paul, Anu, additional, Peskind, Elaine R., additional, Phelps, Mitch A., additional, Putterman, Chaim, additional, Quesenberry, Peter J., additional, Quinn, Joseph F., additional, Ranabothu, Saritha, additional, Rao, Shannon Jiang, additional, Rodriguez-Aguayo, Cristian, additional, Rosenzweig, Anthony, additional, Sabatine, Marc S., additional, Sakhanenko, Nikita A., additional, Saugstad, Julie Anne, additional, Schmittgen, Thomas D., additional, Shah, Neethu, additional, Shah, Ravi, additional, Shedden, Kerby, additional, Shi, Jian, additional, Sood, Anil K., additional, Sopeyin, Anuoluwapo, additional, Spengler, Ryan M., additional, Spetzler, Robert, additional, Srinivasan, Srimeenakshi, additional, Subramanian, Sai Lakshmi, additional, Suthanthiran, Manikkam, additional, Tanriverdi, Kahraman, additional, Teng, Yun, additional, Tewari, Muneesh, additional, Thistlethwaite, William, additional, Tuschl, Thomas, additional, Urbanowicz, Karolina Kaczor, additional, Vickers, Kasey C., additional, Voinnet, Olivier, additional, Wang, Kai, additional, Wei, Zhiyun, additional, Weiner, Howard L., additional, Weiss, Zachary R., additional, Williams, Zev, additional, Wong, David T.W., additional, Woodruff, Prescott G., additional, Xiao, Xinshu, additional, Yan, Irene K., additional, Yeri, Ashish, additional, Zhang, Bing, additional, and Zhang, Huang-Ge, additional

Published
2019
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18. Complex genetic dependencies among growth and neurological phenotypes in healthy children: Towards deciphering developmental mechanisms.

Author

Uechi, Lisa, Jalali, Mahjoubeh, Wilbur, Jayson D., French, Jonathan L., Jumbe, N. L., Meaney, Michael J., Gluckman, Peter D., Karnani, Neerja, Sakhanenko, Nikita A., and Galas, David J.

Subjects
PHENOTYPES, BIOLOGICAL networks, DNA, PLACENTA, INFANTS
Abstract

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 dependencies among the observed phenotypes of fetal and early childhood growth, neurological development, and a number of genetic variants. We have quantified these dependencies using our information theory-based methods. The genetic variants show dependencies with single phenotypes as well as pleiotropic effects on more than one phenotype and thereby point to a large number of brain-specific and brain-expressed gene candidates. These dependencies provide a basis for connecting a range of variants with a spectrum of phenotypes (pleiotropy) as well as with each other. A broad survey of known regulatory expression characteristics, and other function-related information from the literature for these sets of candidate genes allowed us to assemble an integrated body of evidence, including a partial regulatory network, that points towards the biological basis of these general dependencies. Notable among the implicated loci are RAB11FIP4 (next to NF1), MTMR7 and PLD5, all highly expressed in the brain; DNMT1 (DNA methyl transferase), highly expressed in the placenta; and PPP1R12B and DMD (dystrophin), known to be important growth and development genes. While we cannot specify and decipher the mechanisms responsible for the phenotypes in this study, a number of connections for further investigation of fetal and early childhood growth and neurological development are indicated. These results and this approach open the door to new explorations of early human development. [ABSTRACT FROM AUTHOR]

Published
2020
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23. Biological data analysis as an information theory problem: multivariable dependence measures and the shadows algorithm

Author

Luxembourg Centre for Systems Biomedicine (LCSB): Experimental Neurobiology (Balling Group) [research center], Sakhanenko, Nikita A., Galas, David J., Luxembourg Centre for Systems Biomedicine (LCSB): Experimental Neurobiology (Balling Group) [research center], Sakhanenko, Nikita A., and Galas, David J.

Abstract

Information theory is valuable in multiple-variable analysis for being model-free and nonparametric, and for the modest sensitivity to undersampling. We previously introduced a general approach to finding multiple dependencies that provides accurate measures of levels of dependency for subsets of variables in a data set, which is significantly nonzero only if the subset of variables is collectively dependent. This is useful, however, only if we can avoid a combinatorial explosion of calculations for increasing numbers of variables. The proposed dependence measure for a subset of variables, tau, differential interaction information, Delta(tau), has the property that for subsets of tau some of the factors of Delta(tau) are significantly nonzero, when the full dependence includes more variables. We use this property to suppress the combinatorial explosion by following the "shadows" of multivariable dependency on smaller subsets. Rather than calculating the marginal entropies of all subsets at each degree level, we need to consider only calculations for subsets of variables with appropriate "shadows." The number of calculations for n variables at a degree level of d grows therefore, at a much smaller rate than the binomial coefficient (n, d), but depends on the parameters of the "shadows" calculation. This approach, avoiding a combinatorial explosion, enables the use of our multivariable measures on very large data sets. We demonstrate this method on simulated data sets, and characterize the effects of noise and sample numbers. In addition, we analyze a data set of a few thousand mutant yeast strains interacting with a few thousand chemical compounds.

Published
2015

25. Article Expansion of the Kullback-Leibler Divergence, and a New Class of Information Metrics.

Author

Galas, David J., Dewey, Gregory, Kunert-Graf, James, and Sakhanenko, Nikita A.

Subjects
PROBABILITY density function, SYSTEM analysis, INFORMATION processing, INFORMATION theory, DATA analysis
Abstract

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 class of problem. This study takes an approach that simplifies many aspects of these problems by presenting a structured, series expansion of the Kullback-Leibler divergence--a function central to information theory--and devise a distance metric based on this divergence. Using the Möbius inversion duality between multivariable entropies and multivariable interaction information, we express the divergence as an additive series in the number of interacting variables, which provides a restricted and simplified set of distributions to use as approximation and with which to model data. Truncations of this series yield approximations based on the number of interacting variables. The first few terms of the expansion-truncation are illustrated and shown to lead naturally to familiar approximations, including the well-known Kirkwood superposition approximation. Truncation can also induce a simple relation between the multi-information and the interaction information. A measure of distance between distributions, based on Kullback-Leibler divergence, is then described and shown to be a true metric if properly restricted. The expansion is shown to generate a hierarchy of metrics and connects this work to information geometry formalisms. An example of the application of these metrics to a graph comparison problem is given that shows that the formalism can be applied to a wide range of network problems and provides a general approach for systematic approximations in numbers of interactions or connections, as well as a related quantitative metric. [ABSTRACT FROM AUTHOR]

Published
2017
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26. Computational Inference Software for Tetrad Assembly from Randomly Arrayed Yeast Colonies.

Author

Sakhanenko NA, Cromie GA, Dudley AM, and Galas DJ

Subjects
Alleles, Chromosome Segregation, Gene Expression Regulation, Fungal, Meiosis, Recombination, Genetic, Reproducibility of Results, Spores, Fungal, Computational Biology methods, Software, Yeasts physiology
Abstract

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 centromere and allele segregation patterns. Because the method uses only the genomic sequence, it alleviates the need for tetrad-specific barcodes or other genetic modifications to the strains. Using this method, strains derived from randomly arrayed spores can be efficiently grouped back into tetrads., (Copyright © 2019 Sakhanenko et al.)

Published
2019
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