Extracting Reproducible Time-Resolved Resting State Networks Using Dynamic Mode Decomposition
- J. Kunert-Graf, K. Eschenburg, D. Galas, J. Nathan Kutz, S. Rane, Bingni W. Brunton
- Computer SciencebioRxiv
- 8 June 2018
A novel method based on dynamic mode decomposition (DMD) to extract networks from short windows of noisy, high-dimensional fMRI data, allowing RSNs from single scans to be resolved robustly at a temporal resolution of seconds.
Multistability and Long-Timescale Transients Encoded by Network Structure in a Model of C. elegans Connectome Dynamics
- J. Kunert-Graf, E. Shlizerman, Andrew Walker, J. Kutz
- BiologyFrontiers in Computational Neuroscience
- 13 June 2017
The neural dynamics of the nematode Caenorhabditis elegans are experimentally low-dimensional and may be understood as long-timescale transitions between multiple low-dimensional attractors. Previous…
Allele Frequency Mismatches and Apparent Mismappings in UK Biobank SNP Data
- J. Kunert-Graf, Nikita A. Sakhanenko, D. Galas
- BiologybioRxiv
- 3 August 2020
A large set of SNPs for which the UKB MAFs are inconsistent are found, and it is found that they are all associated with identical sequences on different chromosomes, implying that these SNPs are simply mismapped.
Partial Information Decomposition and the Information Delta: A Geometric Unification Disentangling Non-Pairwise Information
- J. Kunert-Graf, Nikita A. Sakhanenko, D. Galas
- Computer ScienceEntropy
- 27 September 2020
This paper shows that the PID and Delta frameworks are largely equivalent, allowing for results in one framework to apply towards open questions in the other, and finds that the approach of Bertschinger et al. is useful for the open Information Delta question of how to deal with linkage disequilibrium.
Expansion of the Kullback-Leibler Divergence, and a new class of information metrics
- D. Galas, T. Dewey, J. Kunert-Graf, Nikita A. Sakhanenko
- Computer ScienceAxioms
- 31 January 2017
This study presents a structured, series expansion of the Kullback-Leibler 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.
The control structure of the nematode Caenorhabditis elegans: Neuro-sensory integration and proprioceptive feedback.
- C. Fieseler, J. Kunert-Graf, J. Kutz
- BiologyJournal of Biomechanics
- 17 July 2017
The Information Content of Discrete Functions and Their Application in Genetic Data Analysis
- Nikita A. Sakhanenko, J. Kunert-Graf, D. Galas
- Computer ScienceJ. Comput. Biol.
- 1 December 2017
This work presents here a direct method for classifying the functional forms of discrete functions of three variables represented in data sets and illustrates the functional description and the classes of a number of common genetic interaction modes.
Towards an information theory of quantitative genetics
- D. Galas, J. Kunert-Graf, L. Uechi, Nikita A. Sakhanenko
- BiologybioRxiv
- 21 October 2019
This paper lays the initial groundwork for a full formulation of quantitative genetics based in information theory, which naturally applies to multi-variable interactions and higher-order complex dependencies, and can be adapted to account for population structure, linkage and non-randomly segregating markers.
Toward an Information Theory of Quantitative Genetics
- D. Galas, J. Kunert-Graf, L. Uechi, Nikita A. Sakhanenko
- BiologyJ. Comput. Biol.
- 31 December 2020
This article argues that revisiting the framework for analysis is important and begins to lay the foundations of an alternative formulation of quantitative genetics based on information theory, and presents information-based measures of the genetic quantities: penetrance, heritability, and degrees of statistical epistasis.
Complexity and Vulnerability Analysis of the C. Elegans Gap Junction Connectome
- J. Kunert-Graf, Nikita A. Sakhanenko, D. Galas
- Computer ScienceEntropy
- 8 March 2017
A view of the gap junction Connectome as consisting of a rather low-complexity network component whose symmetry is broken by the unique connectivities of singularly important rich club neurons, sharply increasing the complexity of the network is supported.
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