Approximate IsoRank for Scalable and Functionally Meaningful Cross-Species Alignments of Protein Interaction Networks.

The IsoRank algorithm of Singh, Xu, and Berger was a pioneering algorithmic advance that applied spectral methods to the problem of cross-species global alignment of biological networks. We develop a new IsoRank approximation that exploits the mathematical properties of IsoRank's linear system to solve the problem in quadratic time with respect to the maximum size of the two protein-protein interaction (PPI) networks. We further propose a refinement to this initial approximation so that the updated result is even closer to the original IsoRank formulation while remaining computationally inexpensive.

A Causally Naïve and Rigid Population Model of Disease Occurrence Given Two Non-Independent Risk Factors.

We describe a computational population model with two risk factors and one outcome variable in which the prevalence (%) of all three variables, the association between each risk factor and the disease, as well as the association between the two risk factors is the input. We briefly describe three examples: retinopathy of prematurity, diabetes in Panama, and smoking and obesity as risk factors for diabetes. We describe and discuss the simulation results in these three scenarios including how the published information is used as input and how changes in risk factor prevalence changes outcome prevalence.

Connectedness of PPI network neighborhoods identifies regulatory hub proteins.

Motivation: With the growing availability of high-throughput protein-protein interaction (PPI) data, it has become possible to consider how a protein's local or global network characteristics predict its function.

Results: We introduce a graph-theoretic approach that identifies key regulatory proteins in an organism by analyzing proteins' local PPI network structure. We apply the method to the yeast genome and describe several properties of the resulting set of regulatory hubs.

An algorithm for modularity analysis of directed and weighted biological networks based on edge-betweenness centrality.

Modularity analysis is a powerful tool for studying the design of biological networks, offering potential clues for relating the biochemical function(s) of a network with the 'wiring' of its components. Relatively little work has been done to examine whether the modularity of a network depends on the physiological perturbations that influence its biochemical state. Here, we present a novel modularity analysis algorithm based on edge-betweenness centrality, which facilitates the use of directional information and measurable biochemical data.

FiberID--a technique to identify fibrous protein subclasses.

Fibrous proteins such as collagen, silk, and elastin play critical biological roles, yet they have been the subject of few projects that use computational techniques to predict either their class or their structure. In this article, we present FiberID, a simple yet effective method for identifying and distinguishing three fibrous protein subclasses from their primary sequences. Using a combination of amino acid composition and fast Fourier measurements, FiberID can classify fibrous proteins belonging to these subclasses with high accuracy by using two standard machine learning techniques (decision trees and Naïve Bayesian classifiers).