000			02904cam a2200349 a 4500
003			EG-GiCUC
005			20250223032537.0
008			200804s2020 ua dh f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.20.03.M.Sc.2020.No.V
100	0		_aNora Abdelhameed Mohamed
245	1	0	_aVisualization and modeling of the virological structure / _cNora Abdelhameed Mohamed ; Supervised Amr A. Badr , Ahmed Farouk Alsadek , Mohamed Nassef
246	1	5	_aتصور ووضع نماذج للهيكل الفيروسى
260			_aCairo : _bNora Abdelhameed Mohamed , _c2020
300			_a66 Leaves : _bcharts , facsimiles ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Computers and Artificial Intelligence - Department of Computer Science
520			_aAssembly of capsid virus is a crucial step in virus life cycle. Without this step, virus would not replicate itself to hijack other cells and its life cycle would end. Many researchers studied virus structural shape and its dynamics to understand the behavior of virus. A small virus capsid contains identical asymmetric units that are packed in regular manner. Every icosahedral virus has two types of symmetry, regular symmetry and noncrystallographic symmetry. One asymmetric unit and some rotation matrices are needed to form the whole capsid. These rotation matrices define the location of adjacent asymmetric unit.This thesis focuses on the structural shape of Icosahedral viruses and prediction of symmetries in their capsids.Two approaches are carried out to study the construction of a crystal asymmetric unit. The first approach predicts the full rotation matrix (4x4 matrix). The second approach predicts the rotation angles and translation vector. In each approach, we are applying convolution neural network (CNN) and fully connected neural network (FNN). Spatial geometry and biological characteristics were collected for each icosahedral capsid virus from the Protein Data Bank (PDB). Using visualization technique, the results were promising; as in the approach that predicts angles, FNN model accuracy reached 89% and reached 84% in CNN model in the same approach. While the second approach had a lower accuracy percentage; as it reached 67% in FNN model and 45% in CNN model. FNN models in general gave better performance in accuracy and 0.25% less in time and 0.90% less memory consumption than CNN models
530			_aIssued also as CD
653		4	_aAsymmetric Unit
653		4	_aNoncrystallgraphic Symmetry
653		4	_aViral Capsids
700	0		_aAhmed Farouk Alsadek , _eSupervisor
700	0		_aAmr A. Badr , _eSupervisor
700	0		_aMohamed Nassef , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aNazla _eRevisor
905			_aShimaa _eCataloger
942			_2ddc _cTH
999			_c77070 _d77070