利用支持向量机的无标记光流体细胞分类器
Label-free Optofluidic Cell Classifier Utilizing Support Vector Machines.
作者信息
Wu Tsung-Feng, Mei Zhe, Lo Yu-Hwa
机构信息
Materials Science and Engineering Program at University of California at San Diego, La Jolla, CA 92093-0418 USA.
出版信息
Sens Actuators B Chem. 2013 Sep;186:327-332. doi: 10.1016/j.snb.2013.06.014.
Abstract
A unique optofluidic lab-on-a-chip device that can measure optically encoded forward scattering signals has been demonstrated. From the design of the spatial pattern, the position and velocity of each cell in the flow can be detected and then a spatial cell distribution over the cross section of the channel can be generated. According to the forward scattering intensity and position information of cells, a data-mining method, support vector machines (SVMs), is applied for cell classification. With the help of SVMs, the multi-dimensional analysis can be performed to significantly increase all figures of merit for cell classification.
摘要
一种能够测量光学编码前向散射信号的独特光流控芯片实验室装置已得到验证。根据空间图案的设计，可以检测流中每个细胞的位置和速度，进而生成通道横截面上的空间细胞分布。根据细胞的前向散射强度和位置信息，采用一种数据挖掘方法——支持向量机（SVM）进行细胞分类。借助支持向量机，可以进行多维分析，以显著提高细胞分类的所有品质因数。
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