Bayesian automatic relevance determination algorithms for classifying gene expression data.
BACKGROUND
We investigate two new Bayesian classification algorithms incorporating feature selection. These algorithms are applied to the classification of gene expression data derived from cDNA microarrays.


RESULTS
We demonstrate the effectiveness of the algorithms on three gene expression datasets for cancer, showing they compare well with alternative kernel-based techniques. By automatically incorporating feature selection, accurate classifiers can be constructed utilizing very few features and with minimal hand-tuning. We argue that the feature selection is meaningful and some of the highlighted genes appear to be medically important.
Li
Yi
Y
Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1TR, UK Microsoft Research, 7 J J Thomson Avenue, Cambridge, CB3 0FD, UK.
Campbell
Colin
C
Tipping
Michael
M
D000465:Algorithms; D001499:Bayes Theorem; D016000:Cluster Analysis; D003110:Colonic Neoplasms / Q000145:classification / Q000235:genetics; D030561:Databases, Nucleic Acid; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D005819:Genetic Markers / Q000235:genetics; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000145:classification / Q000235:genetics; D008957:Models, Genetic; D009369:Neoplasms / Q000145:classification / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010051:Ovarian Neoplasms / Q000145:classification / Q000235:genetics; D010363:Pattern Recognition, Automated; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000145:classification / Q000235:genetics; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods
D005819:Genetic Markers