Biomedical subjects

Aaron Clauset

Publications and source records attributed to Aaron Clauset.

6 recordsLinked to original sources

PubMed2006

Scale invariance in road networks.

We study the topological and geographic structure of the national road networks of the United States, England, and Denmark. By transforming these networks into their dual representation, where roads are vertices and an edge connects two vertices if the corresponding roads ever intersect, we show that they exhibit both topological and geographic scale invariance. That is, we show that for sufficiently large geographic areas, the dual degree distribution follows a power law with exponent 2.2< or = alpha < or =2.4, and that journeys, regardless of their length, have a largely identical structure. To explain these properties, we introduce and analyze a simple fractal model of road placement that reproduces the observed structure, and suggests a testable connection between the scaling exponent and the fractal dimensions governing the placement of roads and intersections.

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PubMed2005

Molecular modeling of mono- and bis-quaternary ammonium salts as ligands at the alpha4beta2 nicotinic acetylcholine receptor subtype using nonlinear techniques.

The neuronal nicotinic acetylcholine receptor (nAChR) has been a target for drug development studies for over a decade. A series of mono- and bis-quaternary ammonium salts, known to be antagonists at nAChRs, were separated into 3 structural classes and evaluated using both self-organizing map (SOM) and genetic functional approximation (GFA) algorithm models. Descriptors from these compounds were used to create several nonlinear quantitative structure-activity relationships (QSARs). The SOM methodology was effective in appropriately grouping these compounds with diverse structures and activities. The GFA models were also able to predict the activities of these molecules. Charge distribution and the hydrophobic free energies were found to be important indicators of bioactivity for this particular class of molecules. These QSAR approaches may be a useful to screen and select in silico new drug candidates from larger compound libraries to be further evaluated in in vitro biological assays.

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PubMed2005

Finding local community structure in networks.

Although the inference of global community structure in networks has recently become a topic of great interest in the physics community, all such algorithms require that the graph be completely known. Here, we define both a measure of local community structure and an algorithm that infers the hierarchy of communities that enclose a given vertex by exploring the graph one vertex at a time. This algorithm runs in time O(k2d) for general graphs when d is the mean degree and k is the number of vertices to be explored. For graphs where exploring a new vertex is time consuming, the running time is linear, O(k). We show that on computer-generated graphs the average behavior of this technique approximates that of algorithms that require global knowledge. As an application, we use this algorithm to extract meaningful local clustering information in the large recommender network of an online retailer.

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PubMed2005

Accuracy and scaling phenomena in Internet mapping.

It was recently argued that sampling a network by traversing it with paths from a small number of sources, as with traceroutes on the Internet, creates a fundamental bias in observed topological features like the degree distribution. We examine this bias analytically and experimentally. For Erdo s-Re nyi random graphs with mean degree c, we show analytically that such sampling gives an observed degree distribution P(k) approximately k(-1) for k less, similarc, despite the underlying distribution being Poissonian. For graphs whose degree distributions have power-law tails P(k) approximately k(-alpha), sampling can significantly underestimate alpha when the graph has a large excess (i.e., many more edges than vertices). We find that in order to accurately estimate alpha, one must use a number of sources which grows linearly in the mean degree of the underlying graph. Finally, we comment on the accuracy of the published values of alpha for the Internet.

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PubMed2004

Finding community structure in very large networks.

The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O (md log n) where d is the depth of the dendrogram describing the community structure. Many real-world networks are sparse and hierarchical, with m approximately n and d approximately log n, in which case our algorithm runs in essentially linear time, O (n log(2) n). As an example of the application of this algorithm we use it to analyze a network of items for sale on the web site of a large on-line retailer, items in the network being linked if they are frequently purchased by the same buyer. The network has more than 400 000 vertices and 2 x 10(6) edges. We show that our algorithm can extract meaningful communities from this network, revealing large-scale patterns present in the purchasing habits of customers.

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PubMedDate not supplied

Supervised self-organizing maps in drug discovery. 1. Robust behavior with overdetermined data sets.

The utility of the supervised Kohonen self-organizing map was assessed and compared to several statistical methods used in QSAR analysis. The self-organizing map (SOM) describes a family of nonlinear, topology preserving mapping methods with attributes of both vector quantization and clustering that provides visualization options unavailable with other nonlinear methods. In contrast to most chemometric methods, the supervised SOM (sSOM) is shown to be relatively insensitive to noise and feature redundancy. Additionally, sSOMs can make use of descriptors having only nominal linear correlation with the target property. Results herein are contrasted to partial least squares, stepwise multiple linear regression, the genetic functional algorithm, and genetic partial least squares, collectively referred to throughout as the "standard methods". The k-nearest neighbor (kNN) classification method was also performed to provide a direct comparison with a different classification method. The widely studied dihydrofolate reductase (DHFR) inhibition data set of Hansch and Silipo is used to evaluate the ability of sSOMs to classify unknowns as a function of increasing class resolution. The contribution of the sSOM neighborhood kernel to its predictive ability is assessed in two experiments: (1) training with the k-means clustering limit, where the neighborhood radius is zero throughout the training regimen, and (2) training the sSOM until the neighborhood radius is reduced to zero. Results demonstrate that sSOMs provide more accurate predictions than standard linear QSAR methods.

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