Alireza Darvish

Interactive visual analysis of time-series microarray data

Estimating dynamic regulatory pathways using DNA microarray time-series can provide invaluable information about the dynamic interactions among genes and result in new methods of rational drug design. Even though several purely computational methods have been introduced for DNA pathway analysis, most of these techniques do not provide a fully interactive method to explore and analyze these dynamic interactions in detail, which is necessary to obtain a full understanding. In this paper, we present a unified modeling and visual approach focusing on visual analysis of gene regulatory pathways over time. As a preliminary step in analyzing the gene interactions, the method applies two different techniques, a clustering algorithm and an auto regressive (AR) model. This approach provides a successful prediction of the dynamic pathways involved in the biological process under study. At this level, these pure computational techniques lack the transparency required for analysis and understanding of the gene interactions. To overcome the limitations, we have designed a visual analysis method that applies several visualization techniques, including pixel-based gene representation, animation, and multi-dimensional scaling (MDS), in a new way. This visual analysis framework allows the user to quickly and thoroughly search for and find the dynamic interactions among genes, highlight interesting gene information, show the detailed annotations of the selected genes, compare regulatory behaviors for different genes, and support gene sequence analysis for the interesting genes. In order to enhance these analysis capabilities, several methods are enabled, providing a simple graph display, a pixel-based gene visualization technique, and a relation-displaying technique among gene expressions and gene regulatory pathways.

Discovering dynamic regulatory pathway by applying an auto regressive model to time series DNA microarray data

In this paper we propose a novel method to extract dynamic regulatory pathways from time-series DNA microarray data. To this aim, first a specialized clustering technique is applied that utilizes the available heuristic information about the biological system to form the clusters. Then, an auto regressive (AR) model is applied to model the interactions among all genes and to predict the gene expressions for the next time steps. We tested the proposed method on the eukaryotic cell cycle data. The results indicate that the proposed method can successfully predict the dynamic pathway involved in this biological process.

System Identification and Nonlinear Factor Analysis for Discovery and Visualization of Dynamic Gene Regulatory Pathways

DNA microarray time-series provide the information vital to estimate the dynamic regulatory pathways and therefore predict the dynamic interaction among genes in time. While dynamic system identification theory has been applied to many fields of study, due to some practical limitations, this theory has been widely used to analyze DNA microarray time series. In this paper, we describe some of these limitations and propose a hierarchical model utilizing nonlinear factor analysis methods to analyze time-series DNA microarray data and identify the dynamic regulatory pathways. The proposed model is applied to model the eukaryotic cell cycle process using a popular dataset of cell cycle time-series. The results indicate that the proposed method can successfully predict the dynamic pathway involved in the process.

Independent component analysis and scoring function based on protein interactions

We describe an approach for discovering biological gene clusters from gene expression data of DNA microarray and scoring the genes based on protein interaction data. Our approach is based on the assumption that many clusters exhibit two properties, i.e., their genes exhibit a similar gene expression profile and the protein products of the genes often interact. Our approach to clustering is based on the independent component analysis model, which uses the ICA algorithm and our approach to scoring is based on number of protein product interactions of the genes within a cluster. We present the results on Saccharomyces cerevisiae gene expression dataset combined with a binary protein interaction data set.

Computational analysis and classification of p53 mutants according to primary structure

Widely used multiple alignment based techniques can give false results for single base mutation as the primary sequence of mutants and that of the wild types are very similar. We present a technique that uses signal processing methods along with biochemical properties of individual amino acids for the analysis of proteins. Each amino acid of mutant protein is replaced with the corresponding biochemical properties and generates a set of biochemical signals. These signals are used to extract signal processing features like complexity, mobility, fractal dimension, and wavelet transformation. In an experimental study of p53 protein, mutants resulting from single mutation of eight residue of the /spl beta/-strand 326-33 to alanine were analyzed for their ability to stimulate transcription, to inhibit the growth of Saos-2 cells, and to repress the promoter of multidrug resistance gene. Our classification results, merely based on the analysis of primary sequences, are matching with those of the experiential studies.

A new hierarchical method for identification of dynamic regulatory pathways from time-series DNA microarray data

A new hierarchical method is proposed to analyze timeseries DNA microarray data to identify dynamic genetic pathways. Initially the hierarchical method applies a specialized clustering technique to incorporate the available heuristic information about biological system. Then, the prototypes of the resulting clusters are used as time-variables to develop an auto regressive model to relate the expression of the prototypes to each other. The resulting model also allows the prediction of gene expressions for the next time steps. The developed AR model can then be used to relate the expression value of each single gene to the genes of other clusters. The proposed method was applied to the cell-cycle dataset containing the DNA microarray time-series of a large number of genes involved in the eukaryotic cell-cycle. The technique resulted to a network of interactions among five clusters of genes in which the genes of each cluster have a biologically-meaningful trend in time.