David J. Galas

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Unifying formalism for multivariate information-related measures: Möbius operators on subset lattices pdf

David J. Galas, Nikita A. Sakhanenko, 2016, ArXiv.

Complexity of networks I: The set-complexity of binary graphs

David J. Galas, Nikita A. Sakhanenko, D. Galas, 2011, Complex..

Complexity of networks II: The set complexity of edge-colored graphs

David J. Galas, Nikita A. Sakhanenko, Tomasz M. Ignac, 2012, Complex..

Multivariate information measures: a unification using M\"obius operators on subset lattices

David J. Galas, Nikita A. Sakhanenko, D. Galas, 2016, 1601.06780.

On Lattices and the Dualities of Information Measures pdf

David J. Galas, Nikita A. Sakhanenko, Benjamin Keller, 2013, ArXiv.

Towards an information theory of quantitative genetics

David J. Galas, Nikita A. Sakhanenko, James M. Kunert-Graf, 2019, bioRxiv.

Relations between the set-complexity and the structure of graphs and their sub-graphs

David J. Galas, Nikita A. Sakhanenko, Tomasz M. Ignac, 2012, EURASIP J. Bioinform. Syst. Biol..

Duplication Models for Biological Networks

Fan Chung Graham, Linyuan Lu, David J. Galas, 2002, J. Comput. Biol..

An integrative approach to inferring biologically meaningful gene modules

Kai Wang, David J. Galas, Ji-Hoon Cho, 2011, BMC Systems Biology.

The interaction of RNA polymerase and lac repressor with the lac control region

David J. Galas, Albert Schmitz, D. Galas, 1979, Nucleic Acids Res..

Symmetries among Multivariate Information Measures Explored Using Möbius Operators

David J. Galas, Nikita A. Sakhanenko, 2019, Entropy.

Biological Information as Set-Based Complexity

Nathan D. Price, Ilya Shmulevich, David J. Galas, 2010, IEEE Transactions on Information Theory.

RELATION BETWEEN THE SET-COMPLEXITY OF A GRAPH AND ITS STRUCTURE

David J. Galas, Nikita A. Sakhanenko, Tomasz Ignac, 2011 .

A duplication growth model of gene expression networks

David J. Galas, T. Gregory Dewey, Ashish Bhan, 2002, Bioinform..

Describing the complexity of systems: Multi-variable "set complexity" and hypergraphs pdf

David J. Galas, Nikita A. Sakhanenko, Alexander Skupin, 2013, ArXiv.

The Information Content of Discrete Functions and Their Application in Genetic Data Analysis

David J. Galas, Nikita A. Sakhanenko, James Kunert-Graf, 2017, J. Comput. Biol..

RCytoscape: tools for exploratory network analysis

David J. Galas, Burak Kutlu, Paul T. Shannon, 2013, BMC Bioinformatics.

Markov Logic Networks in the Analysis of Genetic Data

David J. Galas, Nikita A. Sakhanenko, 2010, J. Comput. Biol..

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

David J. Galas, Nikita A. Sakhanenko, James M. Kunert-Graf, 2017, Axioms.

RNASEQR—a streamlined and accurate RNA-seq sequence analysis program

Chuan Yi Tang, David J. Galas, Kai Wang, 2011, Nucleic acids research.

New methods for finding associations in large data sets: Generalizing the maximal information coefficient (MIC)

David J. Galas, Nikita A. Sakhanenko, Alexander Skupin, 2012 .

Biological Data Analysis as an Information Theory Problem: Multivariable Dependence Measures and the Shadows Algorithm

David J. Galas, Nikita A. Sakhanenko, D. Galas, 2015, J. Comput. Biol..

Set-based complexity and biological information pdf

Nathan D. Price, Ilya Shmulevich, David J. Galas, 2008, ArXiv.

Probabilistic Logic Methods and Some Applications to Biology and Medicine

David J. Galas, Nikita A. Sakhanenko, D. Galas, 2012, J. Comput. Biol..

The Group Theoretic Roots of Information I: permutations, symmetry, and entropy pdf

David J. Galas, D. Galas, 2019, ArXiv.

Multivariate Analysis of Data Sets with Missing Values: An Information Theory-Based Reliability Function

David J. Galas, Nikita A. Sakhanenko, Lisa Uechi, 2019, J. Comput. Biol..

Generalized Dynamic Models of Gene Expression and Gene Classification

David J. Galas, T. Gregory Dewey, 2000 .

Describing the Complexity of Systems: Multivariable "Set Complexity" and the Information Basis of Systems Biology

David J. Galas, Nikita A. Sakhanenko, Alexander Skupin, 2013, J. Comput. Biol..

Complexity and Vulnerability Analysis of the C. Elegans Gap Junction Connectome

David J. Galas, Nikita A. Sakhanenko, James M. Kunert-Graf, 2017, Entropy.

Extracting Reproducible Time-Resolved Resting State Networks Using Dynamic Mode Decomposition

Bingni W. Brunton, David J. Galas, Swati D. Rane, 2018, bioRxiv.

Maximal Extraction of Biological Information from Genetic Interaction Data

David J. Galas, Gregory W. Carter, Timothy Galitski, 2009, PLoS Comput. Biol..

Optimized permutation testing for information theoretic measures of multi-gene interactions

David J. Galas, Nikita A. Sakhanenko, James M. Kunert-Graf, 2021, BMC Bioinform..

Toward an Information Theory of Quantitative Genetics

David J. Galas, Nikita A. Sakhanenko, James M. Kunert-Graf, 2020, J. Comput. Biol..

A Mathematical Analysis ofin VitroMolecular Selection – Amplification

David J. Galas, Fengzhu Sun, Michael S. Waterman, 1996 .

Isothermal reactions for the amplification of oligonucleotides

David J. Galas, D. Galas, J. Van Ness, 2003, Proceedings of the National Academy of Sciences of the United States of America.

An Evaluation of High-Throughput Approaches to QTL Mapping in Saccharomyces cerevisiae

David J. Galas, Nikita A. Sakhanenko, Simon Anders, 2013, Genetics.

Comparing the MicroRNA Spectrum between Serum and Plasma

David J. Galas, David Baxter, Ji-Hoon Cho, 2012, PloS one.

Discovering Pair-Wise Genetic Interactions: An Information Theory-Based Approach

David J. Galas, Nikita A. Sakhanenko, Alexander Skupin, 2014, PloS one.

Chromosomal haplotypes by genetic phasing of human families.

Jared C. Roach, Gustavo Glusman, David J. Galas, 2011, American journal of human genetics.

Dynamic models of gene expression and classification

David J. Galas, T. Gregory Dewey, D. Galas, 2001, Functional & Integrative Genomics.

RNA in circulation: sources and functions of extracellular exogenous RNA in blood

David J. Galas, Paul Wilmes, P. Wilmes, 2014 .

Systems Biology and Emerging Technologies Will Catalyze the Transition from Reactive Medicine to Predictive, Personalized, Preventive and Participatory (P4) Medicine

David J. Galas, Leroy Hood, L. Hood, 2009 .

Systems Biology and Emerging Technologies Will Catalyze the Transition from Reactive Medicine to Predictive , Personalized , Preventive and Participatory ( P 4 ) Medicine

David J. Galas, Leroy Hood, L. Hood, 2009 .

MicroRNA as biomarkers and regulators in B-cell chronic lymphocytic leukemia

David J. Galas, Ji-Hoon Cho, Petr V. Nazarov, 2011, Proceedings of the National Academy of Sciences.

sRNAnalyzer—a flexible and customizable small RNA sequencing data analysis pipeline

David J. Galas, David Baxter, Taek-Kyun Kim, 2017, Nucleic acids research.