Mahdi Vasighi

Assistant Professor at Department of Computer Science and Information Technology

Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran

Contact information



+98 24 3315 3378

Institute for Advanced Studies in Basic Sciences (IASBS), No. 444, Prof. Yousef Sobouti Blvd., Zanjan 45137-66731, Iran

The International Conference on Contemporary Issues In Data Science

Conference homepage

March 5-8, 2019

Institute for Advanced Studies in Basic Sciences (IASBS)

Research Interests

My current researches are focused on a range of problems in structural bioinformatics. In order to model the relationships between the sequences and corresponding structure or biological function, biological sequence coding is considered as one of the main steps to design powerful models (classifiers/predictors) using different machine learning algorithms. Extraction of the relevant features is also another important step in related researches.

Another part of my researches is to design and develop more sophisticated multivariate data modeling and dimensionality reduction methods based on artificial neural networks. Most of the researches in this area are related to vector quantization based methods like the self-organizing map (SOM) which ease the exploratory data analysis in genomics, proteomics, and medical informatics.

Current Projects

Structural Classification of Proteins using Cellular Automata

Establishing structural and functional relationships between sequences in the presence of only the primary sequence information is a key task in biological sequence analysis. One of the main issues is how to interpret and represent protein sequences. There are many feature extraction and representation techniques usually convert the sequence to numerical values and vectors which could be used as input for many machine learning approaches.

Different approaches to extract features from sequences, feature selection and building a powerful classifier are the main subjects in structural classification of proteins.

Self Organizing Maps with Dynamic Structure

The growing self-organizing map (GSOM) possesses effective capability to generate feature maps and visualizing high-dimensional data without pre-determining their size by offering a flexible structure which enable the ability to learn the nonlinear manifolds in high dimensional feature space. Our recent researches focus on defining more reasonable strategies to control and direct the growth of the net in GSOMs. The proposed directed batch growing self organizing map (DBGSOM) uses a growing strategy based on the accumulative error around the candidate boundary neuron and helps to direct the network growth in proper directions.

Data Visualization and Dimensionality Reduction

In this project, we are working on nonlinear mapping for data visualization and dimensionaliy reduction which can resolve the deficiencies of classical SOMs. These deficencies like the static strcture and lack of topology preservation are the central subjects which considered to define more efficient mapping methods with an efficient topology preservation abilities.