Bioinformatics Courses at FAU:

Charles E. Schmidt College of Science
1. Bioinformatics (BSC 6458C) 4 credits (Dr. R. Narayanan)
Prerequisite: Permission of instructor

A practical approach to accessing nucleic/protein databases, management of databases, identification of genes, and electronic expression profiling.

2 . Fractals and Chaos in the Life Sciences (ISC 5451) 3 credits (Dr. L. Liebovitch


Describes the properties of fractals and nonlinear dynamics and shows how they can be used to analyze and understand the structure and function of biological systems such as DNA, proteins, ion channels, nerve cells, blood vessels, the heart, the lung, and the brain.

3. Advanced Functional Genomics (BSC 6936) 3 credits (Dr. E. Orlando)

A lecture and discussion format course that, together with a dry lab, will explore the relatively new fields of functional genomics. Using DNA microarray experiments as a basis, experimental design and data interpretation will be analyzed.

College of Engineering & Computer Science

4. Bioinformatics: Bioengineering Perspectives (BME 6762) 3 credits (Dr. P.S. Neelakanta)
Prerequisite: Engineering/Science B.S. degree

Introduction to bio- and genetic-engineering. Concepts and definitions of molecular biological terms. Bioinformatics—definition and applications. Information resources and databases: Proteins and genomes. Biological sequence analysis and applications. Sequence search/analyses tools and protocols. Bioinformatics versus modern information networks and the World Wide Web.

5. Introduction to Bioengineering (BME 5000) 3 credits (Dr. S. Morgera)
Prerequisite: Engineering/Science B.S. degree

This course provides a broad perspective of Bioengineering as applied to topics in contemporary Biology, Physiology, and Medicine, including Biotechnology and Bioinformatics. The course is designed for graduate students and may be taken by senior undergraduates with permission of the instructor.

6. Artificial Intelligence (CAP 6635) 3 credits (Drs. A. Pandya/ T. Khoshgoftar/X. Zhu)

Prerequisite: COT 4400
The basic concepts, techniques, and applications of artificial intelligence: representations, search strategies, control, communication, deduction, agents, evolutionary computation
and machine learning.

7. Neural Complex and Artificial Neural Networks (EEL 6819) 3 credits (Dr. P.S. Neelakanta)

Multifaceted representation of neural activity in terms of neurobiology, cognitive science, art of computation, cybernetics and physics of statistical mechanics. Neural network modeling mimicking biological neural complex and development of artificial neural networks.

8. Data Mining and Bioinformatics 3 credits (Dr. T. Khoshgoftar)
This is a new course (Fall 2007). Contact instructor.

9. Introduction to Neural Networks (CAP 5615) 3 credits (Dr. A. Pandya)

Prerequisite: CDA 3201C
Brief introduction to biological neural systems. Models of neural mechanisms of learning and memory. Neural net applications to image processing, pattern recognition, machine
learning, optimization problems, and robotics. Hardware implementation issues.

10. Experimental Design and Statistical Inference (PSY 6206).

This course (Psychology) will provide a basic but thorough introduction to experimental designs and statistical inference procedures that are commonly used in the life sciences. The course will include a heavy emphasis on research designs that use Analysis of Variance (ANOVA) as the analytic method. The use and interpretation of statistics will be covered.

11. Statistical Methods for Environmental Sciences. (STA 6206).

This course (Math) will introduce basic probability and statistical distributions, fundamentals of statistical inference, fundamental issues in experiment design, data analysis of treatment-versus-control differences, treatment-versus-control multiple comparisons, simple and multiple linear regression models, analysis of variance. Statistical software Minitab will be used to illustrate statistical methods.