An Epicurean learning approach to gene-expression data classification

Authors:
Andreas Albrecht;Staal A. Vinterbo;Lucila Ohno-Machado
Affiliations:
Computer Science Department, University of Hertfordshire, Hatfield, Herts AL10 9AB, UK;Decision Systems Group, Harvard Medical School, 75 Francis Street, Boston, MA, USA;Decision Systems Group, Harvard Medical School, 75 Francis Street, Boston, MA, USA and Division of Health Sciences and Technology, MIT, Cambridge, MA, USA
Venue:
Artificial Intelligence in Medicine
Year:
2003

Citing 7
Cited 4

Cooling schedules for optimal annealing

Mathematics of Operations Research
The Strength of Weak Learnability

Machine Learning
Robust trainability of single neurons

Journal of Computer and System Sciences
Local Search in Combinatorial Optimization

Local Search in Combinatorial Optimization
Combining the Perceptron Algorithm with Logarithmic Simulated Annealing

Neural Processing Letters
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

IEEE Transactions on Information Theory

Two-stage classification methods for microarray data

Expert Systems with Applications: An International Journal
APPLYING DATA MINING TECHNIQUES FOR CANCER CLASSIFICATION ON GENE EXPRESSION DATA

Cybernetics and Systems
The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming

Artificial Intelligence in Medicine
Generation of comprehensible hypotheses from gene expression data

BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

We investigate the use of perceptrons for classification of microarray data where we use two datasets that were published in [Nat. Med. 7 (6) (2001) 673] and [Science 286 (1999) 531]. The classification problem studied by Khan et al. is related to the diagnosis of small round blue cell tumours (SRBCT) of childhood which are difficult to classify both clinically and via routine histology. Golub et al. study acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). We used a simulated annealing-based method in learning a system of perceptrons, each obtained by resampling of the training set. Our results are comparable to those of Khan et al. and Golub et al., indicating that there is a role for perceptrons in the classification of tumours based on gene-expression data. We also show that it is critical to perform feature selection in this type of models, i.e. we propose a method for identifying genes that might be significant for the particular tumour types. For SRBCTs, zero error on test data has been obtained for only 13 out of 2308 genes; for the ALL/AML problem, we have zero error for 9 out of 7129 genes that are used for the classification procedure. Furthermore, we provide evidence that Epicurean-style learning and simulated annealing-based search are both essential for obtaining the best classification results.