Bioinformatics school training courses in Ann Arbor, MI (public, in-house or online)

This course centers on the construction and use of agent-based adaptive models study phenomena which are prototypical in the social, biological and decision sciences. These models are "agent-based" or "bottom-up" in that the structure placed at the le vel of the individuals as basic components; they are "adaptive" in that individuals often adapt to their environment through evolution or learning. The goal of these models is to understand how the structure at the ind...

Full archival course description>>

This course is an advanced introduction to numerical techniques used in statistics, modeling, and data analysis. Course topics include: background in numerical analysis (concepts of convergence rate and computational complexity), classical numerical techniques (root finding, quadrature), combinatorial enumerations, numerical linear algebra (linear solvers, matrix factorizations, eigenvalue problems), optimization (Newton-Raphson, gradient optimization, EM algorithm, constrained optimization, st...

Full archival course description>>

Techniques and principles for developing application software based on explicit representation and manipulation of domain knowledge, as applied to areas such as pattern matching, problem-solving, automated planning, and natural-language processing. Discussion of major programming approaches used in the design and development of knowledge-based systems.

The course will be about probabilistic models of proteins and nucleic acids, and their uses in molecular biology. The core of the course will be the analysis of sequences and its biological applications such as the searching of large databases for optimal comparisons or homologies of sequences (DNA nucleotide sequences or amino acid sequences for proteins), location of genes on a string of DNA, estimation of phylogenetic trees, structural motif recognition for proteins and RNA. Guest lecturers w...

Full archival course description>>

The course will cover statistical methods used to analyze data in experimental molecular biology, with an emphasis on gene and protein expression array data. Topics: data acquisitions, databases, low level processing, normalization, quality control, statistical inference (group comparisons, cyclicity, survival), multiple comparisons, statistical learning algorithms, clustering, visualization and case studies.