Abstract
Biclustering genes and samples is important to extract knowledge from
gene expression measurements. Current methods are not generative
models allowing model selection and Bayesian framework or are additive
models not explaining mRNA effects and PCR amplification. We
introduce a generative, multiplicative model which assumes realistic
non-Gaussian heavy tail distributions.
| Original language | English |
|---|---|
| Title of host publication | ISMB 2010 Proceedings |
| Number of pages | 1 |
| Publication status | Published - Jul 2010 |
Fields of science
- 101004 Biomathematics
- 101027 Dynamical systems
- 101028 Mathematical modelling
- 101029 Mathematical statistics
- 101014 Numerical mathematics
- 101015 Operations research
- 101016 Optimisation
- 101017 Game theory
- 101018 Statistics
- 101019 Stochastics
- 101024 Probability theory
- 101026 Time series analysis
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102003 Image processing
- 102004 Bioinformatics
- 102013 Human-computer interaction
- 102018 Artificial neural networks
- 102019 Machine learning
- 103029 Statistical physics
- 106005 Bioinformatics
- 106007 Biostatistics
- 202017 Embedded systems
- 202035 Robotics
- 202036 Sensor systems
- 202037 Signal processing
- 305901 Computer-aided diagnosis and therapy
- 305905 Medical informatics
- 305907 Medical statistics
- 102032 Computational intelligence
- 102033 Data mining
- 101031 Approximation theory