Abstract

The analysis of biological sample data based on Raman spectroscopy involves applying the relevant chemometrics pre-processing and creating the statistical model. In this work, genetic algorithms and pipelines were used to study the steps and sequences of the pre-processing for human blood serum Raman spectra form 76 healthy individuals and 84 lung cancer patients for different data analysis models. The models used in this study include support vector machine (linear kernel and nonlinear kernel), multilayer perceptron, and partial least squares discriminant analysis. The results show that the steps and sequence of pre-processing are not immutable for different models. These optimized pipelines are evaluated by execution time and optimization results. The conclusions are that genetic algorithms can optimize the pipeline of pre-processing strategies and classification models to improve data analysis accuracy, and support vector machine models are more suitable for the classification of our lung cancer serum data.

摘要

基于拉曼光谱的生物样本数据分析涉及应用相关化学计量学预处理和创建统计模型。在这项工作中，使用遗传算法和管道研究了76名健康个体和84名肺癌患者的人血清拉曼光谱的预处理步骤和顺序，用于不同的数据分析模型。本研究使用的模型包括支持向量机（线性核和非线性核）、多层感知器和偏最小二乘判别分析。结果表明，对于不同的模型，预处理的步骤和顺序并不是一成不变的。这些优化的管道通过执行时间和优化结果进行评估。