Vision
Computer science and information science are two majors that are used in all current sciences, as knowledge related to medicine and health is now the biggest and the most challenging area of knowledge, Bioinformatics has become a unique academic field of science. Bioinformatics is a multidisciplinary field of science, which incorporates, applied mathematics, statistics, computer science, artificial intelligence, biochemistry and biology to answer the questions aided in understanding cellular and molecular biologic issues.The key areas of research in bioinformatics are:
— Modeling existing structures as symbols of data and information that makes the relationship of concepts and facts more clear.
— Development and enhancement of softwares and computer aided systems to increase the quality of patient treatment and biomedical research.
— Developing new methods to assess models and systems of research in health, data mining, education and management.
— Drug discovery and development
History and Achievements:
In 2011 the School of Advanced Technologies in Medicine started its activities by setting up a research group in the bioinformatics field. In the weekly sessions of this research group the following goals have been followed:
— Training the members by the experts through lecture on the fundamental concepts of biology and computational algorithms
— Evaluating the progress of theses and conducting them by the experts’ comments on the presentations of students
In the recent years significant number of PhD and MSc theses were supervised in this field and some are ongoing. For this purpose, the computational tools required for fast processing of large data is purchased and launched. Also, a great number of professors and experts are collaborating with our faculty in this area.
Selected Thesis:
— PhD Thesis: Seeking an appropriate feature extraction method for breast cancer recurrence prediction based on microarray gene expression data. Supervised by A. Mehridehnavi and Hossein Rabbani.
— PhD Thesis: Using a new adaptive deep learning method for drug discovery with application in investigating Parkinson disease. Supervised by A. Mehridehnavi and A. Fasihi.
— MSc Thesis: Classification and diagnosis of Lymphoma cancer and normal tissues based on gene expression data using artificial neural networks. Supervised by A. Mehridehnavi and M. Salehi.
— MSc Thesis: Implementing of pattern recognition algorithms for design of a method to find genes in prokaryotes. Supervised by A. Mehridehnavi.
— MSc Thesis: Dimensionality reduction on the prediction of breast cancer recurrence by using topological features of the gene network constructed from microarray data. Supervised by A. Mehridehnavi.
Selective Publications:
1- M.R. Sehhati, A. Mehridehnavi, H. Rabbani, “Stable gene signature selection for prediction of breast cancer recurrence using joint mutual information”, IEEE Transaction on Computational Biology and Bioinformatics, 2015; 12(6): 1440-8.
2- Ghasemi F., Fassihi A.,Preze Sunchez H., Mehridehnavi A., “The role of Different Sampling Methods in Improving Biological Activity Prediction Using Deep Belief Network “, journal of chemical information and modeling, DOI: 10.1002/jcc.24671, 2016.
3- Cerón-Carrasco JP, Coronado-Parra T, Imbernón-Tudela B, Banegas-Luna AJ, Ghasemi F., Vegara-Meseguer JM. “Application of Computational Drug Discovery Techniques for Designing New Drugs against Zika Virus”. Drug Designing, 2016.
4- Ghasemi F, Mehri A, Peña-García J, den-Haan H, Pérez-Garrido A, Fassihi A, et al. “Improving Activity Prediction of Adenosine A2B Receptor Antagonists by Nonlinear Models”. Bioinformatics and Biomedical Engineering: Springer; 2015. p. 635-44.
5- Juluri A., Ghasemi F., Pérez-Sánchez H., Murthy R., Murthy N.,” IONTOPHORESIS – Captisol-Enabled(TM) Lipophilic Drug Complex Delivered Transdermally by Iontophoresis “, Drug development and delivery, 2015.
6- Mohammadreza Sehhati, Alireza Mehri Dehnavi, Hossein Rabbani, Shaghayegh Haghjoo Javanmard, “Using protein interaction database and support vector machine to improve gene signatures for prediction of breast cancer recurrence”, Journal of Medical Signals and Sensors, 2013; 3(2): 87-93.
7- Alireza Mehri Dehnavi, Mohammadreza Sehhati, Dr hossein Rabbani, “Hybrid method for prediction of metastasis in breast cancer patients using gene expression signals”, Journal of Medical Signals and Sensors, 2013; 3(2): 79-86.
8- Alireza Mehridehnavi, Leia Ziaei, “Minimal gene selection for classification and diagnosis prediction based on gene expression profile”, Advanced Biomedical Research, 2013; 2(2): 1-5.
9- L Ziaei, AR Mehri, M Salehi “Application of Artificial Neural Networks in Cancer Classification and Diagnosis Prediction of a Subtype of Lymphoma Based on Gene Expression Profile”, Journal of Research in Medical Sciences.
10- A.R. Mehridehnavi, “Classification of the different cancerous animal tissues on the basis of their 1H NMR spectra data using different types of Artificial Neural Networks”, RPS Ressearch in Pharmaceutical Sciences.
11- Malekshahi R, Mehridehnavi A.R, Beigi M, Poorhosein M, “Gene Finding Using HMM In Prokaryotic Genomes”, Modern Genetics Journal.
Open Positions
If you are interested in doing research in one of our fields or related to a specific project, please feel free to send us your unsolicited application including CV and an according motivation letter.