Eugene Stanley

DNA visual and analytic data mining

Describes data exploration techniques designed to classify DNA sequences. Several visualization and data mining techniques were used to validate and attempt to discover new methods for distinguishing coding DNA sequences (exons) from non-coding DNA sequences (introns). The goal of the data mining was to see whether some other, possibly non-linear combination of the fundamental position-dependent DNA nucleotide frequency values could be a better predictor than the AMI (average mutual information). We tried many different classification techniques including rule-based classifiers and neural networks. We also used visualization of both the original data and the results of the data mining to help verify patterns and to understand the distinction between the different types of data and classifications. In particular, the visualization helped us develop refinements to neural network classifiers, which have accuracies as high as any known method. Finally, we discuss the interactions between visualization and data mining and suggest an integrated approach.

Current Flow in Random Resistor Networks: The role of Percolation in Weak and Strong Disorder

We study the current flow paths between two edges in a random resistor network on a L X L square lattice. Each resistor has resistance e(ax) , where x is a uniformly distributed random variable and a controls the broadness of the distribution. We find that: (a) The scaled variable u identical with u congruent to L/a(nu) , where nu is the percolation connectedness exponent, fully determines the distribution of the current path length l for all values of u . For u >> 1, the behavior corresponds to the weak disorder limit and l scales as l approximately L, while for u << 1 , the behavior corresponds to the strong disorder limit with l approximately L(d(opt) ), where d(opt) =1.22+/-0.01 is the optimal path exponent. (b) In the weak disorder regime, there is a length scale xi approximately a(nu), below which strong disorder and critical percolation characterize the current path.

Novel Forecasting Techniques Using Big Data, Network Science and Economics

The combination of theoretical network approach with recently available abundant economic data leads to the development of novel analytic and computational tools for modeling and forecasting key economic indicators. The main idea of this study is to introduce a topological component into economic analysis, consistently taking into account higher-order interactions in the economic network. We present a multiple linear regression optimization algorithm to generate a relational network between individual components of national balance of payment accounts. Our model describes well annual country statistics using the explanatory power and best fits of related global financial and trade indicators. The proposed algorithm delivers good forecasts with high accuracy for the majority of the indicators.

Power laws for cities

The growth and development of urban areas has long been a concern for city planners. In the last few years, scientists have begun to apply models from physics and chemistry to the problem, and have found that cities and urban populations can be viewed as self-organizing systems. Damian Zanette and Susanna Manrubia of the Fritz Haber Institute in Berlin have now applied statistical mechanics to the problem of how cities grow and decline (Phys. Rev. Lett. 1997 79 323). The results indicate that universal laws describe the size and population distributions of cities, providing new insights in the field of urban science.