Faculty : Computer Science and Information Technology Programs
School : Program of Bioinformatics
Prerequisit Course : MTH121
Credit Hours : 3.00
Offered For : Under Graduate
Course Description :
Overview and history of neuro computing, of neuro-computing. Learning laws: Self-adaptation equations, coincidence learning, performance learning, competitive learning, filter learning, spatiotemporal learning. Neural network concepts: Basic definition. connections. processing elements. Feedforward neural networks (non-recurrent neural networks). Back-propagation Learning-Algorithm. Delta Rule. Scaling and Biases. Performance Issues. Associative memories. Hetero-associative, auto-associative and interpolative memories. Bidirectional
associative memories. Counter propagation neural networks. Extreme Learning Machines. Support Vector Machines and Kernels. Kernel definition. Applications in Bioinformatics
BIF328 - Genetic Algorithms
Faculty : Computer Science and Information Technology Programs
School : Program of Bioinformatics
Prerequisit Course : MTH121
Credit Hours : 3.00
Offered For : Under Graduate
Course Description :
Canonical Genetic Algorithm. Basic operators. Selection, Crossover and Mutation. Fitness functions. Replacement strategies. Floating point representations. Uniform and non-uniform mutations. Function optimization. Schema theory. Genetic programming. Tree representations. Applications. Fuzzy logic. Fuzzy rule-based systems. Evolution of fuzzy systems. Genetic learning of neural networks. Feature selection. Clustering using genetic algorithms. Evolution Strategies. Applications in Bioinformatics
BIF329 - Biophysics
Faculty : Computer Science and Information Technology Programs
School : Program of Bioinformatics
Prerequisit Course : PHY121
Credit Hours : 3.00
Offered For : Under Graduate
Course Description :
The course provides a general introduction to quantitative aspects of biological processes and the underlying physical principles. Among the key topics covered in the course are the following: transport processes and rates of biochemical/biophysical reactions (including enzyme kinetics), structure and function of biological macromolecules and macromolecular assemblies, bioenergetics, protein synthesis, mechanism of inheritance, some commonly used experimental techniques in biophysics. BIO463 Machine Learning and Bioinformatics Prerequisites: Bioinformatics, Neural Networks and learning Machines. This course covers the basic applications of machine learning and modeling techniques to biological systems. Topics include gene structure, recognition of DNA and protein sequence patterns, classification, and protein structure prediction. Pattern discovery, Hidden Markov models/support vector machines/neural network/profiles. Protein structure prediction, functional characterization or proteins, functional genomics/proteomics, metabolic pathways/gene networks.
BIF323 - Bio-computing
Faculty : Computer Science and Information Technology Programs
School : Program of Bioinformatics
Prerequisit Course : MTH121
Credit Hours : 3.00
Offered For : Under Graduate
Course Description :
This course provides an introduction to the features of biological data, how that data is organized efficiently in databases, and how existing data resources can be utilized to solve a variety of biological problems. Relational databases, object oriented databases, ontologies, data modeling and description, survey of current biological databases with respect to above, implementation of a database focused on a biological topic. Biopython and R programming. For the Lab part, this Lab emphasizes the hands-on biological data, how that data is organized efficiently in databases, and how existing data resources are utilized to solve a variety of biological problems. Practicing on current biological, Biopython and R programming.
BIF411 - Structural Bioinformatics
Faculty : Computer Science and Information Technology Programs
School : Program of Bioinformatics
Prerequisit Course : BIF321
Credit Hours : 3.00
Offered For : Under Graduate
Course Description :
This course introduces the practical application of structure analysis, database searching and molecular modeling techniques to study protein structure and function. The basic concepts of macromolecular structure are reviewed together with secondary structure calculation and structure-alignment approaches as well as molecular visualization software, and web-based tools. The student will gain practical knowledge in using software techniques to: handle and compare structural information, search the Protein Data Bank site, analyze protein structure and generate 3D structures on the basis of homology.
