SGN6206 Genomic Signal
Processing
Lectures
1. A biological introduction 3.12
2. Classification of disease subtype based on microarray data
4.12
3. Nonlinear Prediction of
Gene Expressions from microarray experiments
10.12
4. Statistical inference with
MDL for nonlinear models
11.12
5. Gene feature selection
17.12
References for
Lectures 2-5:
I.Tabus,
J. Rissanen, J. Astola. Nonlinear
signal modelling and structure selection with applications to genomics. to
be published in ADVANCES ON NONLINEAR SIGNAL AND IMAGE PROCESSING, Edited
by Steve Marshal and Giovanni Sicuranza
I. Tabus and J.
Astola. Gene feature selection.
In:
Genomic Signal Processing and Statistics, (E.R. Dougherty, I. Shmulevich,
J. Chen, Z.J. Wang, eds.), Hindawi Publishing Corporation, pp. 67-92, 2005.
6. Sum-of-Exponential models
for time series microarray data
18.01
7. Genetic networks inferred
from time series of gene expression data
7.01
8. Nonlinear modelling of
protein expressions in protein arrays
8.01
9. Complexity of DNA sequence
14.01
10. Universal models with
memory for genomic sequence analysis
15.01
11. Recap: Questions for exam
21.01
Lecture 1.pdf