Jyotsna Kasturi

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).

Identification of Diaryl Ether-Based Ligands for Estrogen-Related Receptor α as Potential Antidiabetic Agents

Estrogen-related receptor α (ERRα) is an orphan nuclear receptor that has been functionally implicated in the regulation of energy homeostasis. Herein is described the development of diaryl ether based thiazolidenediones, which function as selective ligands against this receptor. Series optimization provided several potent analogues that inhibit the recruitment of a coactivator peptide fragment in in vitro biochemical assays (IC(50) < 150 nM) and cellular two-hybrid reporter assays against the ligand binding domain (IC(50) = 1-5 μM). A cocrystal structure of the ligand-binding domain of ERRα with lead compound 29 revealed the presence of a covalent interaction between the protein and ligand, which has been shown to be reversible. In diet-induced murine models of obesity and in an overt diabetic rat model, oral administration of 29 normalized insulin and circulating triglyceride levels, improved insulin sensitivity, and was body weight neutral. This provides the first demonstration of functional activities of an ERRα ligand in metabolic animal models.

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

An online analysis and information fusion platform for heterogeneous biomedical informatics data

Current research in biomedical informatics involves analysis of multiple heterogeneous data sets. This includes patient demographics, clinical and pathology data, treatment history, patient outcomes as well as gene expression, DNA sequences and other information sources such as gene ontologies. Analysis of these data sets could lead to better disease diagnosis, prognosis, treatment and drug discovery. However, the extent of knowledge that can be extracted from individual data sets is limited Recently, there has been a lot of focus on techniques that analyze genomic data sources in an integrated manner through information fusion. This places a need for an online platform to analyze biomedical informatics data sets using these techniques. We present here an online data warehouse to perform data exploration and analysis across heterogeneous biomedical informatics data sets with the aid of information fusion. The prototype platform is available at http://biogeowarehouse.cse.psu.edu.

IRINI: Random group allocation of multiple prognostic factors

Statistically sound experimental design in pharmacology studies ensures that the known prognostic factors, if any, are equally represented across investigational groups to avoid bias and imbalance which could render the experiment invalid or lead to false conclusions. Complete randomization can be effective to reduce bias in the created groups especially in large sample size situations. However, in small studies which involve only few treatment subjects, as in preclinical trials, there is a high chance of imbalance. The effects of this imbalance may be removed through covariate analysis or prevented with stratified randomization, however small studies limit the number of covariates to be analyzed this way. The problem is accentuated when there are multiple baseline covariates with varying scales and magnitudes to be considered in the randomization, and creating a balanced solution becomes a combinatorial challenge. Our method, IRINI, uses an optimization technique to achieve treatment to subject group allocation across multiple prognostic factors concurrently. It ensures that the created groups are equal in size and statistically comparable in terms of mean and variance. This method is a novel application of genetic algorithms to solve the allocation problem and simultaneously ensure quality, speed of the results and randomness of the process. Results from preclinical trials demonstrate the effectiveness of the method.

Identifying Conserved Discriminative Motifs

The identification of regulatory motifs underlying gene expression is a challenging problem, particularly in eukaryotes. An algorithm to identify statistically significant discriminative motifs that distinguish between gene expression clusters is presented. The predictive power of the identified motifs is assessed with a supervised Naive Bayes classifier. An information-theoretic feature selection criterion helps find the most informative motifs. Results on benchmark and real data demonstrate that our algorithm accurately identifies discriminative motifs. We show that the integration of comparative genomics information into the motif finding process significantly improves the discovery of discriminative motifs and overall classification accuracy.