Teaching Interests

BSC 491/L/591/L - Essential Bioinformatics (course materials)
BSC 492/692 - Perl Programming for Bioinformatics (course materials)
Statistical Analysis of Microarray Data
Bioinformatics Databases

Research Interests

• Gene Regulatory Networks Construction: A gene regulatory network (GRN) is a collection of genes in a cell which interact with each other (indirectly through their RNA and protein expression products) and with other substances in the cell. Genes can be viewed as nodes in this network. Several mathematical models of GRNs have been developed, including Boolean networks, Petri nets, Bayesian networks, graphical Gaussian models, Stochastic, and Process Calculi. We are trying to develop new algorithms or tools for gene network construction.
  
  
• EST Data Analysis and Genome Annotation: Expressed Sequence Tags (ESTs) are short and error-prone DNA sequences. They provide an important resource for comparative and functional genomic studies and represent a reliable information for the annotation of genomic sequences. Advances in bioinformatics causes daily generation of ESTs in the form of large datasets. Therefore, efficient bioinformatics approaches are required to analyze data and extract useful information. We developed a pipeline for EST data analysis and expanding our ESTMD database for storing annotation information.
  
  
• Microarray Data Analysis: I am interested in analyzing microarray data using existing methods and developing new algorithms or tools to find new makers genes. We are trying to develop new algorithms or tools for microarray data clustering, marker gene identification, and gene network. We are using both commercial (GeneSpring) and open source (Bioconductor) packages in our group and our aim is to develop new packages to facilitate the analysis process.
  
  
• Machine Learning: Several classification and feature selection methods have been studied for the identification of differentially expressed genes in microarray data. Classification methods such as SVM, RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods have been used in recent studies. We study and compare error rates and accuracy of some of the common used classification, clustering, and feature selection methods. We also develop application for performing methods such as SVM Classifier.
  
  
• Biological Application and Database Development: With huge amount of biological data generated daily, it is important to build biology databases to manage biology data. We are currently working on several databases: Gofetcher, ESTMD, Riboapt, ....

Personal webpage

Representative Publications

Mehdi Pirooznia, Ping Gong, Xin Guan, Laura Inouye, Kuan Yang, Edward J. Perkins, and Youping Deng: Cloning, analysis and functional annotation of expressed sequence tags from the Earthworm (Eisenia fetida) BMC Bioinformatics 2007, 8:S7:S7.

Gong, Ping, Guan, Xin, Inouye, Laura S., Pirooznia, Mehdi, Indest, Karl J., Athow, Rebecca S., Deng, Youping, and Perkins, Edward J. Toxicogenomic Analysis Provides New Insights into Molecular Mechanisms of the Sublethal Toxicity of 2,4,6-Trinitrotoluene in (Eisenia fetida) Environ. Sci. Technol., 2007,  10.1021/es0716352

Mehdi Pirooznia, Youping Deng: Efficiency of Hybrid Normalization of Microarray Gene Expression: A Simulation Study. 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07), pp. 739-744, 2007.

Mehdi Pirooznia, Vijayaraj Ngarajan and Youping Deng: GeneVenn – A Web Application for Comparing Gene Lists Using Venn Diagrams. Bioinformation, 2007, 1(10), 420-422

Venkata Thodima, Mehdi Pirooznia and Youping Deng: RiboaptDB: A Comprehensive Database of Ribozymes and Aptamers. BMC Bioinformatics 2006, 7:S2:S6.

Mehdi Pirooznia and Youping Deng: SVM Classifier - a comprehensive java interface for support vector machine classification of microarray data. BMC Bioinformatics 2006, 7:S4:S25.