The Computational Biology Research Center

The Computational Biology Research Center (CBRC) was founded in 2011 by Dr. Fatemeh Zare-Mirakabad , a faculty member of the Amirkabir University of Technology. The purpose of this center is to be a place for students and professors who are interested in computational topics of bioinformatics field to come together. This center Has played a significant role in promoting the scientific level of students and researchers by creating a relaxed and ambient environment. In this center, there are 14 bachelor students, 55 master students, 11 Ph.D. students and 4 researchers gathered to work and exchange up-to-date ideas in bioinformatics field.

Research areas

Coronavirus disease

Structural bioinformatics


Sequence in bioinformatics

Biological network

In the news

Workshop Video

13 Aug, 2021

Sequence Comparison without Alignment: The SpaM Approaches

2nd ICoBi Workshop

Workshop Video

12 Jun, 2021

Predicting Risk of Cardiovascular Disease Using Machine Learning Methods

2nd ICoBi Workshop


ClustalW & BLAST

BLAST is a powerful tool to find sequences in a database. ClustalW is a general-purpose multiple-sequence alignment program for DNA or protein sequences.

SVM Application in Gene expression

We can use Support Vector Machine models to examine whether the new sample has cancer or not by using gene expression of genes involved in the disease

SVM Application in Protein

We can use Support Vector Machine models in protein problems like Secondary structure prediction, Fold recognition, Cleavage site identification, and RNA-binding proteins.


The outward shift of clarithromycin binding to the ribosome in mutant Helicobacter pylori strains

Farzaneh Salari, Fatemeh Zare Mirakabad, Mehdi Sadeghi
14 August 2020

The Assessment of Histone Acetylation marks in the Vicinity of Transcription Factor Binding Sites in Human CD4+ T Cells using Information Theory Methods

Nafiseh Banirazi Motlagh, Bahram Mohammadpour Esfahani, Behnoosh Ashrafi, et al.
Computational Biology and Chemistry
28 February 2020




Traditional drug discovery methods are costly and time-consuming. The use of the existing approved drugs for treating another disease is called drug repositioning, a common strategy to overcome traditional drug discovery issues.



TranDTA is the first method that applies transformers to extract feature of protein sequence and uses transformer representations in drug target binding affinity (DTBA) prediction.



The data set used in the paper contains the PPI networks of E. coli and S. cerevisiae which are extracted from the Database of Interacting Proteins (DIP). To label the proteins as essential/non-essential, the essential genes data of these species are collected from DEG database.