.png){kind=icon}
返回
Mass spectrometry cancer data classification using wavelets and genetic algorithm
.png){kind=icon}
OA
AI
DOI：10.1016/j.febslet.2015.11.019.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper introduces a hybrid feature extraction method applied to mass spectrometry (MS) data for cancer classification. Haar wavelets are employed to transform MS data into orthogonal wavelet coefficients. The most prominent discriminant wavelets are then selected by genetic algorithm (GA) to form feature sets. The combination of wavelets and GA yields highly distinct feature sets that serve as inputs to classification algorithms. Experimental results show the robustness and significant dominance of the wavelet-GA against competitive methods. The proposed method therefore can be applied to cancer classification models that are useful as real clinical decision support systems for medical practitioners. (C) 2015 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.
Keyword:
Mass spectrometry data
Cancer classification
Wavelet transformation
Genetic algorithm
Feature extraction
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
2.3W
被引数:
3.8W
机构
引用论文
.png){kind=icon}
NIR and mass spectra classification: Bayesian methods for wavelet-based feature selectionNIR和质谱分类: 基于小波的特征选择的贝叶斯方法
PREAI
分享收藏分享收藏
查看更多内容