Computational Systems Biology - BME 211
The course covers:
- Algorithms to integrate high-throughput genomics datasets.
- Probablistic graphical models to infer gene activity in normal and diseased cell states.
- Incorporating gene regulatory networks into machine-learning approaches.
- Review and participation in DREAM challenges to learn about (and contribute!) winning solutions to open molecular biology problems.
Students are graded on:
- 2 written assignments
- 1 oral team presentation covering a winning method in one of the DREAM competitions
- 1 oral team presentation covering the design and application of their own method to perform in a DREAM competition.
Testing equation writing in this wiki:
- Message from a variable v to a factor a is the product of all messages from other factors b:
- $$\mu_{v \rightarrow a}(x_v)=\prod_{b \in N(v) \setminus a}{\mu_{b \rightarrow v}(x_v)}$$
- Message from factor a to variable v:
- $$\mu_{a \rightarrow v}=\sum_{u \in N(a) \setminus v}{ P(x_v,x_u)*\mu_{u \rightarrow a} } $$
Instructors and Assistants
Class Web Page