Master of Science in Computer Science with a Concentration in Bioinformatics
Lawrence Technological University is pleased to announce Bioinformatics as an exciting new concentration in the Master of Science in Computer Science program.
Bioinformatics is the blending of mathematics, statistics and computer science to analyze biological, biochemical and biophysical data. The explosion of genomic information resulting from the Human Genome Project has resulted in the need for the creation and maintenance of large databases. Bioinformatics has many applications in the areas of cancer detection, the design of new drugs, discovering cures for genetic diseases, cloning threatened species, creating new bio-materials, developing disease-resistant crops, influenza surveillance and vaccine strain selection.
Objective
To offer an applied Master of Science in Computer Science program that give students computational tools and skills to explore, understand and analyze vast amounts of biological data. The curriculum emphasizes analytical skills and in-depth technical knowledge blending mathematics, computer science and biology.
MSCS Features
The MSCS program is open to students who have a computer science undergraduate degree and to non-computer science undergraduates who work in the field. Students with a non-computer science bachelor's degree may be required to take one or more pre-core courses in order to gain sufficient preparation for the successful completion of the program.
Program Details
If a student meets the pre-core requirements and has no transfer credits the following courses are required for the degree with a concentration in bioinformatics:
- MCS5303 - Intro to Database Systems
- MCS5503 - Intelligent Systems
- MCS5603* - Introduction to Bioinformatics
- MCS5613* - Genomics
- MCS6033* - Statistical Methods
- MCS6323 - Distributed Database Systems
- MCS6613* - Protein and DNA Sequence Analysis
- MCS6623* - Data Warehousing in Bioinformatics
- MCS 7033 - Collaborative Research Project 2
- MCS 7013 - Collaborative Research Project 1
* Courses under development
A total of 10 courses, 30 semester credit hours.
Special Requirements
Due to the unique nature of the degree concentration there are no free electives in the program.
Admission
In order to be eligible for admission, the prospective graduate student should:
- Hold a bachelor's degree from an accredited institution of higher education with a minimum 3.00 grade point (out of 4.00)
- Submit a completed application to LTU Office of Admissions
- Have all official transcripts from each post-secondary institution attended sent to the LTU Office of Admissions
Transfer Credit
At most two classes (no more than 6 hours) may be transferred into the program. Classes considered for transfer credit must be at the graduate level, related to the concentration or computer science, have 3.00 grade or higher and received the approval of the advisor.
Selected Course Descriptions
MCS 5303 - Intro to Database Systems (3 cr.)
Must have permission of department chair. Design and implementation of relational, hierarchical and network database system. Query/update data language, conceptual data model, physical storage methods, database system architecture and normal forms. Database security and integrity. Relational database systems are emphasized. A project involving an on-line database system is normally assigned, No credit given after MIS6113. Lecture 3 hrs.
MCS 5503 - Intelligent Systems (3 cr.)
Prerequisite: Permission of department chair. Introduction to artificial intelligence and computational intelligence. Problem solving by searching. Optimization methods. Knowledge representation and reasoning. Machine learning. Multi-agent systems. Pattern recognition. Introduction to evolutionary computation. Introduction to artificial neural networks. Fuzzy logic. Lecture 3 hrs.
MCS 6323 - Distributive Database Systems (3 cr.)
Advanced topics in artificial intelligence and computational intelligence. Advanced evolutionary computation. Advanced neural networks. Advance fuzzy logic systems. Introduction to neuro-fuzzy systems and soft computing. Practical applications to wireless devices, web programming, robotics and data mining. Lecture 3 hrs.
MCS 5603 - Introduction to Bioinformatics (3 cr.)
MCS 5613 - Genomics (3 cr.)
The integration of Bioinformatics with the latest laboratory techniques for genome sequencing and genome analysis. Topics include methods used to collect genomic data; accessing the major genomic databases; various nucleotide configuration algorithms and how they work.
MCS 6603 - Statistical Methods (3 cr.)
Introduction to the theory of statistics and some of its applications, use of mathematical models to predict outcomes, data representation, probability, sampling theoretical distributions, estimating, correlation, time series, number usage. (For engineering technology students only.)
MCS 7013 - Collaborative Research Proj 1 (3 cr.)
Must have permission of program director. Initiation of work on a large-scale computer science team or project at the student's workplace. Students work closely with a faculty member and an industry representative.
MCS 7033 - Collaborative Research Proj 2 (3 cr.)
Completion of the computer science project began in MCS7013.
MCS 6623 - Data Warehousing and Mining in Bioinformatics (3 cr.)
This course provides practical techniques used to build a warehouse including requirements definitions, extract-transformation-loads of data, query applications and executive information systems. Data mining algorithms and techniques that identify trends in data stored in a warehouse as applied to Bioinformatics will be covered. Data migration tools, data design tools, data mining tools, and OLAP will be discussed.
To contact Dr. Gus Azar