Medizinische Universität Graz - Research portal
Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Schimek, MG.
Penalized binary regression for gene expression profiling.
METHODS INFORM MED. 2004; 43(5): 439-444.
Doi: 10.1267/METH04050439
Web of Science
PubMed
FullText
FullText_MUG
- Leading authors Med Uni Graz
- Schimek Michael
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- Abstract:
- OBJECTIVES: A typical bioinformatics task in microarray analysis is the classification of biological samples into two alternative categories. A procedure is needed which, based on the expression levels measured, allows us to compute the probability that a new sample belongs to a certain class. METHODS: For the purpose of classification the statistical approach of binary regression is considered. Highdimensionality and at the same time small sample sizes make it a challenging task. Standard logit or probit regression fails because of condition problems and poor predictive performance. The concepts of frequentist and of Bayesian penalization for binary regression are introduced. A Bayesian interpretation of the penalized log-likelihood is given. Finally the role of cross-validation for regularization and feature selection is discussed. RESULTS: Penalization makes classical binary regression a suitable tool for microarray analysis. We illustrate penalized logit and Bayesian probit regression on a well-known data set and compare the obtained results, also with respect to published results from decision trees. CONCLUSIONS: The frequentist and the Bayesian penalization concept work equally well on the example data, however some method-specific differences can be made out. Moreover the Bayesian approach yields a quantification (posterior probabilities) of the bias due to the constraining assumptions.
- Find related publications in this database (using NLM MeSH Indexing)
- Bayes Theorem -
- Computational Biology -
- Gene Expression Profiling -
- Oligonucleotide Array Sequence Analysis - classification
- Find related publications in this database (Keywords)
- Bayes
- bioinformatics
- classification
- cross-validation
- logit regression
- penalization
- prediction
- probit regression