|
|
Advanced
Algorithms and Models for
Computational Biology
|
Announcements |
- Starting date: January 18, 2006
- Class lectures: Mondays & Wednesdays from 3:00-4:20pm
- Location: WEH 4615A
- Recitation:
- Textbook (suggested but not required):
- Durbin et al, Biological Sequence Analysis.
People |
Instructors:
- Eric Xing, Wean Hall 4127, x8-2559, Office hours: Wednesday 16:20-17:20,
- Ziv Bar-Joseph, Wean Hall 4107, x8-8595, Office hours: Monday 16:20-17:30
Class Assistant:
- Monica
Hopes
Course Description |
Dramatic advances in experimental
technology and computational analysis are fundamentally transforming
the basic nature and goal of biological research. The emergence of new
frontiers in biology, such as systems biology and evolutionary
genomics, is demanding new methodologies that can confront quantitative
issues of substantial computational and mathematical sophistication.
Machine learning and probabilistic modeling represent the methods of
choice for designing systems that can integrate, comprehend, query vast
body of heterogeneous biological data based on well-founded statistical
principles. They
provide a systematic computational framework for large-scale
statistical
analysis of dynamic, noisy and dependent experimental data, a
convenient vehicle to adopt
the Bayesian philosophy whereby one can formally incorporate biological
prior
knowledge to the models, and a firm foundation on which to design
composite predictive
and simulation models for biological data from heterogeneous sources.
This course focuses on modern machine learning
methodologies
for
computational problems in molecular biology and genetics, including
probabilistic modeling, inference and learning algorithms, pattern
recognition, data integration, time series analysis, active learning,
etc. We will discuss classical approaches and latest methodological
advances in the context of the following biological problems: 1)
Analysis of high throughput biological data, such as gene expression
data, focusing on issues ranging from data acquisition to pattern
recognition and classification. 2) Computational genomics, focusing on
gene finding, motifs detection and sequence evolution. 3) Medical and
populational genetics, focusing on polymorphism analysis, linkage
analysis, pedigree and genetic demography, 4) Molecular and regulatory
evolution, focusing on phylogenetic inference and regulatory network
evolution, and 5) Systems biology, concerning how to combine sequence,
expression and other biological data sources to infer the structure and
function of different systems in the cell.
Page links |