Daemen Anneleen, Gevaert Olivier, De Moor Bart
ESAT, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium.
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:5411-5. doi: 10.1109/IEMBS.2007.4353566.
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.
目前，癌症的临床管理基于文献（临床研究）中的经验数据或临床医生的专业知识。最近，微阵列技术出现了，它有可能彻底改变癌症和其他疾病的临床管理。微阵列能够同时测量数千个基因的表达水平，这些表达水平可能反映诊断或预后类别以及对治疗的敏感性。本文的目的是研究作为日常临床决策支持基础的临床数据，能否与尚未证明其提供患者定制治疗潜力的微阵列数据，使用最小二乘支持向量机进行有效结合。
