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.
Sequence in bioinformatics
In the news
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.
Identifying and controlling adverse drug reactions is a complex problem in the pharmacological field. Despite the studies done in different laboratory stages, some adverse drug reactions are recognized after being released, such as Rosiglitazone. Due to such experiences, pharmacists are now more interested in using computational methods to predict adverse drug reactions.
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.