Course Descriptions
This is the general introductory course in bioinformatics. The main techniques covered in this course are related to sequence analysis and include: gene identification, genome sequencing, sequence comparison, database searching, and phylogenetic tree analysis. Molecular biology is also introduced including: the central dogma of molecular biology, DNA sequences, and protein sequences. Students will be introduced to all of the biology necessary to understand the applications of bioinformatics algorithms and software taught in this course.
This course is designed for bioinformatics students. The industry standard in statistical software, which will be taught in this course, is R and S++. The application of basic algorithms and the theory behind the statistical analysis will be covered. Extensive examples and small projects will be used in order to learn how to use R and Java to accomplish bioinformatics tasks. Topics covered also include: sample analysis, interval-censored survival data analysis, longitudinal data analysis, multivariate analysis, theory of distributions in statistics, and experiment and design.
This course teaches the students how to elucidate the structure of a biopolymer using related modeling tools and algorithms in bioinformatics. The targeted areas are in protein structure modeling, structure based drug design, drug screening, cheminformatics, and binding prediction. Students will learn the principles and applications of each of the algorithms and programs used in structure modeling.
This course focuses on the study and implementation of methods for data mining and machine learning. Particular attention is given to those methods which are useful in the analysis of gene expression data from genome comparisons, microarray experiments, and protein function prediction. Students will gain practical skills in addition to theoretical knowledge, especially in the area of microarray data analysis. We will use the
R and
Bioconductor packages for the microarray data analysis and
MATLAB for other implementation tasks. All three tools are widely used in industry and academia.
Imaging informatics is an emerging area from the intersection of biological science, medical practice, health care, computer algorithms, and information sciences. Naturally, it belongs to bioengineering, although it is developed and practiced in many other departments and hospitals, especially in the field of radiology.
Datamining of biological and medical data is an emerging area that is becoming increasingly popular in bioinformatics. Biological and medical phenomenons are being studied with high throughput methods that generate large amount of data. To understand the complicated and highly interactive nature of biology from this data, advanced datamining methods and algorithms are required. This course will focus on the basic knowledge of datamining and how it is applied and modified in order to adjust to special characteristics of biological and medical datamining.
Databases are used extensively in biological/medical data storage and analysis. Specific types of databases that are common and widely used include gene and protein sequence, protein structure, protein interaction, metabolic pathways, compounds and drugs, literature, medical records, and many others. Mastering the basic principles and design methods of databases is fundamental in bioinformatics training. In this course we are introduce how database design and application are used in the biomedical field. Students are not required to have prior database knowledge, but should be familiar with the basics of computer science. Fluency in at least one programming language (as well as cursory knowledge of at least one other), familiarity with common operating systems such as Windows XP and Unix/Linux, and experience using online biological and medical databases such as NCBI's PubMed and BLAST, PFAM, SWISS-PROT, etc. are basic requirements.
