The 'subsequent artificial neural network' (SANN) approach might bring more classificatory power to ANN-based DNA microarray analyses.
BACKGROUND
Human decisions often proceed in two steps. Initially those most preferred are chosen followed by a subsequent choice of these preferences. Applying one artificial neural network (ANN), a classification is limited to the preselection process. The final categorization is only possible by a subsequent ANN that distinguishes the pre-chosen classes. Existing strategies using coupled ANNs are discussed and a new approach particularly suited for multiclass classification problems is introduced ('Subsequent ANN', SANN).


RESULTS
Evaluating a simulated data base comprising 3 classes, classification results of SANN were obviously superior to those achieved by ANN. To evaluate a real-world data base the microarray benchmark GCM (14 classes) was chosen. The ANN results reached 72%, comparable to previous results. Using SANN, up to 81% of the tumors were correctly classified.


BACKGROUND
Programs used in this work and numerical results are available upon request.
Linder
Roland
R
Institute for Medical Informatics, Medical University of Luebeck, 23538 Luebeck, Germany. linder@imi.uni-luebeck.de <linder@imi.uni-luebeck.de>
Dew
Dawn
D
Sudhoff
Holger
H
Theegarten
Dirk
D
Remberger
Klaus
K
Pöppl
Siegfried J
SJ
Wagner
Mathias
M
D000465:Algorithms; D016000:Cluster Analysis; D005103:Expert Systems; D020869:Gene Expression Profiling / Q000379:methods*; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software