Integration of clinical and microarray data with kernel methods.
Currently, the clinical management of cancer is based on empirical data from the literature (clinical studies) or based on the expertise of the clinician. Recently microarray technology emerged and it has the potential to revolutionize the clinical management of cancer and other diseases. A microarray allows to measure the expression levels of thousands of genes simultaneously which may reflect diagnostic or prognostic categories and sensitivity to treatment. The objective of this paper is to investigate whether clinical data, which is the basis of day-to-day clinical decision support, can be efficiently combined with microarray data, which has yet to prove its potential to deliver patient tailored therapy, using Least Squares Support Vector Machines.
Daemen
Anneleen
A
ESAT, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium. anneleen.daemen@esat.kuleuven.be
Gevaert
Olivier
O
De Moor
Bart
B
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D015510:Clinical Medicine / Q000379:methods; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D009363:Neoplasm Proteins / Q000032:analysis*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018511:Systems Integration
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins