Faculty : Computer Science and Information Technology Programs

School : Program of Bioinformatics

Course Description :

An introduction to the principles of heredity in diploid organisms, fungi, bacteria, and viruses. Mendelian inheritance; population genetics; quantitative genetics; linkage; sex determination; meiotic behavior of chromosome aberrations, gene structure, regulation, and replication; genetic code. Emphasis is on molecular genetics

Faculty : Computer Science and Information Technology Programs

School : Program of Bioinformatics

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

Faculty : Computer Science and Information Technology Programs

School : Program of Bioinformatics

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

Faculty : Computer Science and Information Technology Programs

School : Program of Bioinformatics

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.

Faculty : Computer Science and Information Technology Programs

School :

Course Description :

The course gives an overview of the fundamental concepts of the fields of genomics and proteomics. Genomics is the study of the functions and interactions of the genes in a genome whereas proteomics is defined as the study of all the proteins expressed by the genome. The genome and the proteome are intimately linked between a complex pathway of transcription and translation, which principally involves mRNA processing, protein folding and posttranslational modifications. Both genomics and proteomics incorporate areas of biotechnology, bioinformatics and biology, and utilize a multitude of methods and techniques to study gene and protein expression profiles of cells and whole biological systems