Raj Acharya

An information theoretic approach for analyzing temporal patterns of gene expression

MOTIVATION Arrays allow measurements of the expression levels of thousands of mRNAs to be made simultaneously. The resulting data sets are information rich but require extensive mining to enhance their usefulness. Information theoretic methods are capable of assessing similarities and dissimilarities between data distributions and may be suited to the analysis of gene expression experiments. The purpose of this study was to investigate information theoretic data mining approaches to discover temporal patterns of gene expression from array-derived gene expression data. RESULTS The Kullback-Leibler divergence, an information-theoretic distance that measures the relative dissimilarity between two data distribution profiles, was used in conjunction with an unsupervised self-organizing map algorithm. Two published, array-derived gene expression data sets were analyzed. The patterns obtained with the KL clustering method were found to be superior to those obtained with the hierarchical clustering algorithm using the Pearson correlation distance measure. The biological significance of the results was also examined. AVAILABILITY Software code is available by request from the authors. All programs were written in ANSI C and Matlab (Mathworks Inc., Natick, MA).

Morphological pyramids with alternating sequential filters

The aim of this paper is to find a relationship between alternating sequential filters (ASF) and the morphological sampling theorem (MST) developed by Haralick et al. (1987). The motivation behind this approach is to take advantage of the computational efficiency offered by the MST to implement morphological operations. First, we show alternative proofs for opening and closing in the sampled and unsampled domain using the basis functions. These proofs are important because they show that it possible to obtain any level of a morphological pyramid in one step rather than the traditional two-step procedure. This decomposition is then used to show the relationship of the open-closing in the sampled and unsampled domain. An upper and a lower bound, for the above relationships, are presented. Under certain circumstances, an equivalence is shown for open-closing between the sampled and the unsampled domain. An extension to more complicated algorithms using a union of openings and an intersection of closings is also proposed. Using the Hausdorff metric, it is shown that a morphologically reconstructed image cannot have a better accuracy than twice the radius of the reconstruction structuring element. Binary and gray scale examples are presented.

Clustering of diverse genomic data using information fusion

Motivation: Genome sequencing projects and high-through-put technologies like DNA and Protein arrays have resulted in a very large amount of information-rich data. Microarray experimental data are a valuable, but limited source for inferring gene regulation mechanisms on a genomic scale. Additional information such as promoter sequences of genes/DNA binding motifs, gene ontologies, and location data, when combined with gene expression analysis can increase the statistical significance of the finding. This paper introduces a machine learning approach to information fusion for combining heterogeneous genomic data. The algorithm uses an unsupervised joint learning mechanism that identifies clusters of genes using the combined data. Results: The correlation between gene expression time-series patterns obtained from different experimental conditions and the presence of several distinct and repeated motifs in their upstream sequences is examined here using publicly available yeast cell-cycle data. The results show that the combined learning approach taken here identifies correlated genes effectively. The algorithm provides an automated clustering method, but allows the user to specify apriori the influence of each data type on the final clustering using probabilities. Availability: Software code is available by request from the first author. Contact: jkasturi@cse.psu.edu

Robust snake model

In this paper, we propose a new deformable model a robust snake model, which solves the primary problems suffered by the conventional snake, such as contour initialization, proper internal parameter setting and the limited capture range of the external energy. A reformulated internal energy is used to serve the smoothness of snake contour without a contraction of the contour. The external energy combines both region and edge information to enlarge the capture range, and also reduces the requirement of initial contour. Both synthetic and real gray-level images are selected to evaluate the performance of the proposed model. Its implementation show it robust, fast and accurate. Initial experimental results are encouraging.

Erratum to “Unsupervised Two-Way Clustering of Metagenomic Sequences”

The reference in the originally published paper reads, “S. Nasser, A. Breland, F. Harris, and M. Nicolescu, “Metagenome fragment classification using n-mer frequency profiles,” in Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS ‘08)*, pp. 1–6, New York, NY, USA, 2008.” However, the correct reference for the citation is “Gail Rosen, Elaine Garbarine, Diamantino Caseiro, Robi Polikar, and Bahrad Sokhansanj, “Metagenome fragment classification using N-mer frequency profiles,” Advances in Bioinformatics, Volume 2008 (2008).”

Impact of mobility prediction on the temporal stability of MANET clustering algorithms

Scalability issues for routing in mobile ad hoc networks (MANETs) have been typically addressed using hybrid routing schemes operating in a hierarchical network architecture. Several clustering schemes have been proposed to dynamically identify and maintain hierarchy in MANETs. To achieve significant performance gains, it is important that the underlying clustering scheme is able to identify stable clusters such that the cost associated with maintaining the clustered architecture is minimized. In this paper, we study the impact of mobility prediction schemes on the temporal stability of the clusters obtained using a mobility-aware clustering framework. We investigate the performance of the prediction schemes with respect to Gauss-Markov, Random Waypoint, and Reference Point Group mobility models under varying network and mobility conditions. Our results indicate that while mobility prediction significantly improves temporal stability of the clusters, an accurate mobility tracking algorithm need not always lead to an accurate mobility prediction scheme.

A Mobility-Prediction-Based Relay Deployment Framework for Conserving Power in MANETs

There has been a growing interest in designing mobile systems consisting of special relay nodes whose mobility can be controlled by the underlying network. In this paper, we consider the design of a heterogeneous mobile ad hoc network (MANET) consisting of two kinds of mobile nodes-traditional nodes with limited energy and a few controllable mobile relay nodes with relatively abundant energy resources. We propose a novel relay deployment framework that utilizes mobility prediction and works in tandem with the underlying MANET routing protocol to optimally define the movement of the relay nodes. We present two instances of the relay deployment problem, together with the solutions, to achieve different goals. Instance 1, termed Min-Total, aims to minimize the total energy consumed across all the traditional nodes during data transmission, while instance 2, termed Min-Max, aims to minimize the maximum energy consumed by a traditional node during data transmission. Our solutions also enable the prioritization of individual nodes in the network based on residual energy profiles and contextual significance. We perform an extensive simulation study to understand the trade-offs involved in deploying an increasing fraction of such relay nodes in the network. We also investigate the performance of the proposed framework under different mobility prediction schemes. Results indicate that even when the relay nodes constitute a small fraction of the total nodes in the network, the proposed framework results in significant energy savings. Further, we observed that while both the schemes have their potential advantages, the differences between the two optimization schemes are clearly highlighted in a sparse network.

Statistical analysis of morphological openings

Statistical analysis of morphological openings is carried out to study noise-suppression and edge-preserving properties of binary and gray-scale structuring elements. Based on the fact that basis functions are a general representation of any morphological mapping that is translation-invariant and increasing, it is shown that a statistical analysis using this representation is feasible and more general than the threshold decomposition approach. >

On the Use of Nodes with Controllable Mobility for Conserving Power in MANETs

We explore the idea of using relay nodes with controllable mobility as intermediate hops for reducing the power consumption in a mobile ad hoc network (MANET). We formulate the relay positioning problem and propose four variations of a simple algorithm to compute the optimal position and the movement of the relay nodes. Results from a preliminary simulation study indicate that deployment of relay nodes does result in considerable power savings in MANETs.

Steady state distribution for stochastic knapsack with bursty arrivals

In this letter, we develop a methodology for obtaining an approximate steady state occupancy distribution for a multiclass stochastic knapsack with bursty call arrivals, and exponential holding times. Preliminary results indicate that the proposed technique is effective in studying the knapsack behavior.