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Papers by Kevin Bassler

Identifying functionally-cohesive gene communities from large data sets of expression data for in... more Identifying functionally-cohesive gene communities from large data sets of expression data for individual genes is a key approach to understanding the molecular components of biological processes. Here, we compare the accuracy of twelve different approaches to infer gene co-expression networks and then find gene communities within the networks. Among the approaches used are ones involving a recently developed clustering method that identifies communities by maximizingGeneralized Modularity Density(Qg). RNA-Seq data from 691 samples ofS. cerevisiae(yeast) are analyzed. These data have been obtained from organisms grown under diverse environmental and developmental conditions and encompass varied mutant lines. To assess the accuracy of different approaches, we introduce a statistical measure, the Average Adjusted Rand Index (AARI) score, which compares their results to Gene Ontology (GO) term associations. Inferring gene networks using theContext Likelihood of Relatedness(CLR) and sub...

ABSTRACTThe structure of neural circuitry plays a crucial role in brain function. Previous studie... more ABSTRACTThe structure of neural circuitry plays a crucial role in brain function. Previous studies of brain organization generally had to trade off between coarse descriptions at a large scale and fine descriptions on a small scale. Researchers have now reconstructed tens to hundreds of thousands of neurons at synaptic resolution, enabling investigations into the interplay between global, modular organization, and cell type-specific wiring. Analyzing data of this scale, however, presents unique challenges. To address this problem we applied novel community detection methods to analyze the synapse-level reconstruction of an adult fruit fly brain containing over 20 thousand neurons and 10 million synapses. Using a machine-learning algorithm, we find the most densely connected communities of neurons by maximizing a generalized modularity density measure. We resolve the community structure at a range of scales, from large (on the order of thousands of neurons) to small (on the order of ...

Bulletin of the American Physical Society, 2017

Although machine learning methods (e.g., cluster analysis) are increasingly being integrated into... more Although machine learning methods (e.g., cluster analysis) are increasingly being integrated into visual analytical applications for identifying complex patterns (e.g., patient subgroups) in large data sets, such approaches typically generate a single best model based on optimizing an objective function. However, comparison of models in the vicinity of the optimal model can enable analysts to explore tradeoffs among key model parameters, important when exploring large datasets. Here we describe the user interface features critical for vicinity exploration, and demonstrate its efficacy in the use of a network layout algorithm to explore a large dataset related to precision medicine.

New Journal of Physics, 2018

Submitted for the MAR11 Meeting of The American Physical Society Frequency of Relevant Nodes with... more Submitted for the MAR11 Meeting of The American Physical Society Frequency of Relevant Nodes with Different Function Classes in Critical Boolean Networks 1 SHABNAM HOSSEIN, MATTHEW REICHL, KEVIN E. BASSLER, University of Houston-Boolean networks have two phases of dynamical behavior, fixed and chaotic, depending on the update functions of the nodes. Boolean functions can be categorized by their symmetry properties, which are related to their canalization properties. Canalization is a type of network robustness, which was first introduced to explain the stability of phenotype expression of biological systems. For networks with 3 inputs per node, the 256 possible Boolean functions can be divided into 14 classes that correspond to the group orbits of rotation plus parity. For critical networks at the boundary of the fixed and chaotic phases, we analytically derive the frequency of the different types of Boolean functions among the relevant nodes that control the dynamics. By setting up a set of differential equations that determines the relevant nodes through a pruning process, we can find the average number of nodes in each of the classes. Then, considering the effects of fluctuations, the probability distribution of the number of relevant nodes is accurately derived. We find that in critical networks the frequency of relevant nodes is inversely correlated with canalization.

Physical Review Letters, 2004

APS Meeting …, 2010

Uniform sampling of graphs from a given degree sequence is a fundamental component of measurement... more Uniform sampling of graphs from a given degree sequence is a fundamental component of measurements on networks, with applications ranging from epidemics through social networks to Internet modeling. Existing graph sampling methods are ill-controlled, with typically unknown ...

Using Boolean networks as prototypical examples, the role of symmetry in the dynamics of heteroge... more Using Boolean networks as prototypical examples, the role of symmetry in the dynamics of heterogeneous complex systems is explored. We show that symmetry of the dynamics, especially in critical states, is a controlling feature that can be used to both greatly simplify analysis and to characterize different types of dynamics. Symmetry in Boolean networks is found by determining the frequency at which the various Boolean output functions occur. Classes of functions occur at the same frequency. These classes are orbits of the controlling symmetry group. We find the nature of the symmetry that controls the dynamics of critical random Boolean networks by determining the frequency of output functions utilized by nodes that remain active on dynamical attractors. This symmetry preserves canalization, a form of network robustness. We compare it to a different symmetry known to control the dynamics of an evolutionary process that allows Boolean networks to organize into a critical state. Our results demonstrate the usefulness and power of using symmetry to characterize complex network dynamics, and introduce a novel approach to the analysis of heterogeneous complex systems.

Journal of Physics: Complexity, 2022

Understanding the underlying structure of a gene regulatory network is crucial to understand the ... more Understanding the underlying structure of a gene regulatory network is crucial to understand the biological functions of genes or groups of genes. A common strategy to investigate it is to find community structure of these networks. However, methods of finding these communities are often sensitive to noise in the gene expression data and the inherent stochasticity of the community detection algorithms. Here we introduce an approach for identifying functional groups and their hierarchical organization in gene co-expression networks from expression data. A network describing the relatedness in the expression profiles of genes is first inferred using an information theoretic approach. Community structure within the inferred network is found by using modularity maximization. This community structure is further refined using three-body structural correlations to robustly identify important functional gene communities. We apply this approach to the expression data of E. coli genes and ide...

JMIR Medical Informatics, 2020

Background When older adult patients with hip fracture (HFx) have unplanned hospital readmissions... more Background When older adult patients with hip fracture (HFx) have unplanned hospital readmissions within 30 days of discharge, it doubles their 1-year mortality, resulting in substantial personal and financial burdens. Although such unplanned readmissions are predominantly caused by reasons not related to HFx surgery, few studies have focused on how pre-existing high-risk comorbidities co-occur within and across subgroups of patients with HFx. Objective This study aims to use a combination of supervised and unsupervised visual analytical methods to (1) obtain an integrated understanding of comorbidity risk, comorbidity co-occurrence, and patient subgroups, and (2) enable a team of clinical and methodological stakeholders to infer the processes that precipitate unplanned hospital readmission, with the goal of designing targeted interventions. Methods We extracted a training data set consisting of 16,886 patients (8443 readmitted patients with HFx and 8443 matched controls) and a repl...

Physical review. E, Statistical, nonlinear, and soft matter physics, 2015

In common descriptions of phase transitions, first-order transitions are characterized by discont... more In common descriptions of phase transitions, first-order transitions are characterized by discontinuous jumps in the order parameter and normal fluctuations, while second-order transitions are associated with no jumps and anomalous fluctuations. Outside this paradigm are systems exhibiting "mixed-order" transitions displaying a mixture of these characteristics. When the jump is maximal and the fluctuations range over the entire range of allowed values, the behavior has been coined an "extreme Thouless effect." Here we report findings of such a phenomenon in the context of dynamic, social networks. Defined by minimal rules of evolution, it describes a population of extreme introverts and extroverts, who prefer to have contacts with, respectively, no one or everyone. From the dynamics, we derive an exact distribution of microstates in the stationary state. With only two control parameters, N(I,E) (the number of each subgroup), we study collective variables of inter...

Acta Crystallographica Section A Foundations of Crystallography, 2007

Submitted for the MAR08 Meeting of The American Physical Society Nonstationary increments and var... more Submitted for the MAR08 Meeting of The American Physical Society Nonstationary increments and variable diffusion processes in financial markets JOSEPH L. MCCAULEY, KEVIN E. BASSLER, GEMUNU H. GUNARATNE, University of Houston, U OF H ECONOPHYSICS GROUP COLLABORATION Fat tailed returns distributions and Hurst exponent scaling for financial markets have been reported for more than a decade. The sliding interval technique used in those analyses implicitly assumes that the increments are stationary, an assumption that generally contradicts the facts that the increments are uncorrelated. We show that the data exhibit nonstationary, uncorrelated increments, implying diffusive dynamics with a variable diffusion coefficient, but there is no evidence for either fat tails or Hurst exponent scaling in daily FX returns.

Submitted for the MAR11 Meeting of The American Physical Society Eigenvalue Spectra of Random Geo... more Submitted for the MAR11 Meeting of The American Physical Society Eigenvalue Spectra of Random Geometric Graphs 1 AMY NY-BERG, KEVIN E. BASSLER, University of Houston-The spectra of the adjacency matrix and the graph Laplacian of networks are important for characterizing both their structural and dynamical properties. We investigate both spectra of random geometric graphs, which describe networks whose nodes have a random physical location and are connected to other nodes that are within a threshold distance. Random geometric graphs model transportation grids, wireless networks, as well as biological processes. Using numerical and analytical methods we investigate the dependence of the spectra on the connectivity threshold. As a function of the number of nodes we consider cases where the average degree is held constant and where the connectivity threshold is kept at a fixed multiple of the critical radius for which the graph is almost surely connected. We find that there exists an eigenvalue separation phenomenon causing the distribution to change form as the graph moves from well connected to sparsely connected. For example, the Laplacian spectra of well connected graphs exhibit a Gaussian envelope of integer values centered about the mean connectivity and superimposed on a real valued distribution. As connectivity decreases, the distribution shifts and includes an accumulation of eigenvalues near zero.

The effects of twin boundaries (TBs) on the complex interaction between magnetism and superconduc... more The effects of twin boundaries (TBs) on the complex interaction between magnetism and superconductivity in slightly electron-doped Ba(Ca)(FeAs)2 superconductors are investigated. The spatial distributions of the magnetic, superconducting and charge density orders near two different types of TBs are calculated. We find that TBs corresponding to a 90 • lattice rotation in the a-b plane enable magnetic domain walls to form with only a small effective Coulomb interaction between valance electrons, and that superconductivity is enhanced at such TBs. Contrastingly, we find that superconductivity is suppressed at TBs corresponding to an asymmetrical placement of As atoms with respect to the Fe atoms in the a-b plane.

The graph Laplacian spectra of networks are important for characterizing both their structural an... more The graph Laplacian spectra of networks are important for characterizing both their structural and dynamical properties. As a prototypical example of networks with strong correlations, we investigate the spectra of random geometric graphs (RGGs), which describe networks whose nodes have a random physical location and are connected to other nodes within a threshold distance r. RGGs model transportation grids, wireless networks, and biological processes. The spectrum consists of two parts, a discrete part consisting of a collection of integer valued delta function peaks centered about the average degree and a continuous part that exhibits the phenomenon of eigenvalue separation. We examine the behavior of eigenvalue separation for large network size N in several scaling regimes based on the parameter α such that N α r = c is constant. We identify a transition at α = 1/3, above which the separated peaks get closer together as N increases and separation is eventually lost, but below which the peaks get farther apart. Also, an approximation for the expected number of separated peaks is given in terms of N and the average degree and we show that the expected number of peaks scales as N α .

Dynamical critical behavior of a prototypical heterogeneous complex system, random Boolean networ... more Dynamical critical behavior of a prototypical heterogeneous complex system, random Boolean networks, is studied. Using analytical arguments, we show that the networks, at the boundary between their frozen and chaotic dynamical phases, display universal critical behavior in their attractor period distribution, which has the functional form of a decaying power-law. Using a known result that nodes relevant to the dynamics on attractors at criticality can be divided into separate components, we analyze the structure of these relevant components and how their dynamics combine to find the distribution of attractor periods. This is accomplished by mapping the problem to the enumeration of binary Lyndon words. We show that the attractor period distribution becomes scale-free in the large network limit with a decay described by a critical exponent of 1. Results of numerical simulations that support this finding, but that also show that substantial finite-size corrections exist, will also be presented. The universal nature of this behavior is demonstrated by comparison to that of the evolved critical state achieved through the playing of an adaptive game that selects for diversity of node behavior.

We study the universality and robustness of variants of the simple model of superconducting vorte... more We study the universality and robustness of variants of the simple model of superconducting vortex dynamics first introduced by Bassler and Paczuski in Phys. Rev. Lett. 81, 3761 (1998). The model is a coarse-grained model that captures the essential features of the plastic vortex motion. It accounts for the repulsive interaction between vortices, the pining of vortices at quenched disordered locations in the material, and the over-damped dynamics of the vortices that leads to tearing of the flux line lattice. We report the results of extensive simulations of the critical ``Bean state" dynamics of the model. We find a phase diagram containing four distinct phases of dynamical behavior, including two phases with distinct Self Organized Critical (SOC) behavior. Exponents describing the avalanche scaling behavior in the two SOC phases are determined using finite-size scaling. The exponents are found to be robust within each phase and for different variants of the model. The differe...

Computing Research Repository - CORR, 2009

We show that the capacity of a complex network that models a city street grid to support congeste... more We show that the capacity of a complex network that models a city street grid to support congested traffic can be optimized by using routes that collectively minimize the maximum ratio of betweenness to capacity in any link. Networks with a heterogeneous distribution of link capacities and with a heterogeneous transport load are considered. We find that overall traffic congestion and average travel times can be significantly reduced by a judicious use of slower, smaller capacity links.