Search for: motahhari--abolfazl
0.007 seconds

    A Simulation for Better Understanding of Integrin Clustering and Activation

    , M.Sc. Thesis Sharif University of Technology Shams, Shahab (Author) ; Motahhari, Abolfazl (Supervisor)
    Today, computer simulations are employed prevalently by researchers to understand biological processes. The tendency to use computational methods has been increased recently due to the high costs and errors of experimental methods. Integrins are membrane proteins that mechanically attach cells to the extra cellular matrix (ECM) and derive some behaviors such as cell migration. Moreover, integrins have biochemical functions. They transduce environment signals and trigger chemical pathways. As a result, integrins regulate cell shape, motility, etc. The investigation of integrin behavior in a real environment is very difficult due to the presence of many other proteins that interfere with the... 

    Semi-supervised Breast Cancer Subtype Clustering Using Microarray Datasets

    , M.Sc. Thesis Sharif University of Technology Vasei, Hamed (Author) ; Motahhari, Abolfazl (Supervisor)
    Gene expression microarrays can be used for precision medicine and targeted therapies. The data generated by microarrays are high-dimensional causing statistical inference of any parameter a daunting task. In this thesis, it is shown that regardless of high-dimensional datasets produced by microarrays, the inference can be robust in the sense that random selection of features results in the same conclusion as far as the number of selected features are chosen appropriately. Stratifying patients with breast cancer based on their gene expression levels shows that patient subtypes are almost independent of the feature selection strategy. Moreover, using less noisy datasets coming from RNAseq... 

    A Thesis Submitted in Partial Fulfillment of the Requirement for the Degree of Master of Science in Electrical Engineering

    , M.Sc. Thesis Sharif University of Technology Mohammadi, Mahsa (Author) ; Jahed, Mehran (Supervisor) ; Motahhari, Abolfazl (Co-Advisor)
    Dnase I Hypersensitive Sites (DHSs) are known as comprehensive markers of DNA regulatory elements. The main function of regulatory elements is repressing or enhancing transcription of genes. Hence, the recruitment of the data is prevalent in many studies of genome. One of the applications of this data is to utilize it to predict active regulatory regions (Transcription Factor Binding Sites).There are different means to do this, divided in three major groups: first, the methods only use the number of DNase-seq reads that surround a candidate binding site. While robust, these methods do not reflect the shape of the signal. A second strategy uses a variety of approaches to model and identify... 

    Evaluation of Base Calling Methods in Next Generation Sequencing

    , M.Sc. Thesis Sharif University of Technology Gharibi, Hadi (Author) ; Hossein Khalaj, Babak (Supervisor) ; Motahhari, Abolfazl (Supervisor)
    In the mid twentieth century by discovering the existence of genetic strands and understanding their role in diseases and phenotypes of species, research initiated to decipher their content. Sequencing of the first human genome at early twenty-first century paved the way to study and even cure complex human deseases having genetic origin. Next Generation Sequencing (NGS) Technologies have significantly reduced the expenses and the timing complexity of DNA Sequencing and this has an improving trend. In this thesis, we evaluate Base Calling methods, a critical step in analyzing next generation sequencing information and deals with massive sequencing data. Base Calling tries to optimally detect... 

    Prediction of Protein Ligand Binding Affinity Using Deep Networks

    , M.Sc. Thesis Sharif University of Technology Gholamzadeh Lanjavi, Atena (Author) ; Kalhor, Hamid Reza (Supervisor) ; Motahhari, Abolfazl (Co-Supervisor)
    Protein-ligand binding affinity is extremely important for finding new candidates in drug discovery and computational biochemistry. One of the physical characteristics for protein ligand interactions has been dissociation constant (KD) which can be obtain experimentally. However, there have been tremendous efforts to predict KD using modeling and computational approaches for protein-ligand interactions. In this project, we have exploited Convolutional Neural Network (CNN) model based on KDeep design, PDBBind version 2016 refined set training data, and examining it with KDeep core set test data. In order to modify KDeep,instead of 24 rotations (0, 90, 180 and 270 degrees in selection of two... 

    Inferring Relation between World and Iranian Populations from Microarray Data

    , M.Sc. Thesis Sharif University of Technology Saberi, Sasan (Author) ; Hossein Khalaj, Babak (Supervisor) ; Motahhari, Abolfazl (Supervisor)
    One of the branches of genetic studies is population genetics. Each population has its own characteristics due to its evolutionary history, cultural characteristics and geography, which distinguish it from other populations. Scientific and technological advances in recent decades have led to the production of new generation sequencing machines and the creation of large genetic data. These data contain important genetic information and answers to many questions about the origin of humans, the history of populations and their evolutionary process. More and better understanding of the human genome and the distance between populations can help to better understand biological mechanisms and deal... 

    On Improving Scalability of Blockchain Systems Using Coding and Redundancy Methods

    , M.Sc. Thesis Sharif University of Technology Badihi, Ahmad Reza (Author) ; Motahhari, Abolfazl (Supervisor) ; Maddah Ali, Mohammad Ali (Supervisor)
    Blockchains are not scalable by design, and it is known to be the most important barrier in the way of development of these systems. One of the main approaches to this problem is sharding, that is under development in industry and academia. Sharding scales the system up by reducing redundancy, that makes blockchains vulnerable in terms of security. In this paper, we will study the effect of sharding on availability of these systems, and will show that sharding can magnify the unavailability of the service, and introduce an adversary threat model that takes real concerns of availability in today’s Internet like DoS attacks into account. We also introduce a basic unavailability-resistant...