The linear neuron as marker selector and clinical predictor in cancer gene analysis.
OBJECTIVE
The problem of gene selection has been extensively studied in a number of scientific works using various kinds of methods. However, the application of a linear neuron is a novel approach possessing several advantages. In this work we propose to study the behavior of such a linear neuron, appropriately adapted and trained to the problem of gene selection in the DNA microarray experiment.


METHODS
We explore the proposed approach in terms of an accuracy evaluation criterion, which is used to assess the performance of the proposed methodology, but we also evaluate the produced results in terms of cluster quality and survival prediction. Cluster quality reflects the ability of the method to select differentially expressed genes, which in turn leads to better clustering and survival prediction.


RESULTS
We directly compare the proposed methodology with RFE-SVM, a well known and broadly accepted method demonstrating remarkable performance on various data sets of clinical interest.


CONCLUSIONS
Conducted computational experiments show that the proposed approach can be efficiently used within the field of gene selection producing high-quality results in terms of accuracy and robustness.
Blazadonakis
Michalis E
ME
Technical University of Crete, Department of Electronic and Computer Engineering, University Campus, Chania Crete 73100, Greece. mblazad@ier.forthnet.gr
Zervakis
Michalis
M
D014408:Biomarkers, Tumor / Q000032:analysis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009363:Neoplasm Proteins / Q000032:analysis*; D009369:Neoplasms / Q000175:diagnosis* / Q000378:metabolism / Q000401:mortality*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D011379:Prognosis; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D015996:Survival Rate
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins