Bioinformatics & System Biology Research & Educational Group

School of Advanced Technologies in Medicine

Isfahan University of Medical Sciences

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. In this regard, the computational tools required for fast processing of large data is purchased and launched. Also according to the reality that a great number of professors and experts are collaborating with our faculty in this area.
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The fast growth of biological data has produced a huge amount of information. It is obvious that without using new information technology it is not possible to manage this data. Although 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.

The fast growth of biological data has produced a huge amount of information. It is obvious that without using new information technology it is not possible to manage this data. Although 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 novel field of science, which incorporates, applied mathematics, statistics, computer science, artificial intelligence, biochemistry and biology to answer the questions and aided in 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 computer aided systems to increase the quality of patient treatment and biomedical research.
  • Bioinformatics work flow management systems.
  • Developing ways to assess models and systems of research in health, data mining, education and management.
  • Drug lead identification and optimization, using computer, for high-throuput docking and quantitative biological activity prediction.


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Selected Publications

Selected Publications

  • 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: Extracting gene regulatory modules using biclustring on microarray data. Supervised by A. Mehridehnavi and M. Mohammadbeigi.
  • 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.
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lodGWAS: a software package for genome-wide association analysis of biomarkers with a limit of detection

Ahmad Vaez, Peter J. van der Most, Bram P. Prins, Harold Snieder,Edwin van den Heuvel, Behrooz Z. Alizadeh, Ilja M. Nolte Bioinformatics, Volume 32, Issue 10, 15 May 2016, Pages 1552–1554, https://doi.org/10.1093/bioinformatics/btw021

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Stable gene signature selection for prediction of breast cancer recurrence using joint mutual information

Sehhati, Mohammadreza, Alireza Mehridehnavi, Hossein Rabbani, and Meraj Pourhossein.

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Improving Activity Prediction of Adenosine A2B Receptor Antagonists by Nonlinear Models

Fahimeh GhasemiAlireza MehriJorge Peña-GarcíaHelena den-HaanAlfonso Pérez-GarridoAfshin FassihiHoracio Péréz-Sánchez

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A novel feature ranking method for prediction of cancer stages using proteomics data

Ehsan Saghapour, Saeed Kermani , Mohammadreza Sehhati Published: September 21, 2017https://doi.org/10.1371/journal.pone.0184203

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Prediction of metastasis in advanced colorectal carcinomas using CGH data

Ehsan Saghapour 1- Mohammadreza Sehhati 2 1. Department of Biomedical Engineering, School of advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran 2. Department of Biomedical Engineering, School of advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran, Medical Imaging & Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran Received 4 March 2017, Revised 30 May 2017, Accepted 20 June 2017, Available online 21 June 2017.

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An in silico Post-GWAS Analysis of C-Reactive Protein Loci Suggests an Important Role for Interferons

Vaez A; Jansen R; Prins BP; Hottenga JJ; de Geus EJ; Boomsma DI; Penninx BW; Nolte IM; Snieder H; Alizadeh BZ Circ Cardiovasc Genet. 2015; 8(3):487-97 (ISSN: 1942-3268)

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Using protein interaction database and support vector machine to improve gene signatures for prediction of breast cancer recurrence

Sehhati, Mohammad Reza, Alireza Mehri Dehnavi, Hossein Rabbani, and Shaghayegh Haghjoo Javanmard.

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Bioinformatics & System Biology Research & Educational Group

School of Advanced Technologies in Medicine

Isfahan University of Medical Sciences

 
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