Molecule Specific Normalization for Protein and Metabolite Biomarker Discovery

被引:0
作者
Trabelsi, Ameni [1 ]
Shi, Biyun [2 ]
Wei, Xiaoli [2 ]
Frigui, Hichem [1 ,5 ]
Zhang, Xiang [2 ,6 ]
McClain, Craig [3 ,4 ,7 ]
Shahrajooihaghighi, Aliasghar [1 ]
机构
[1] Univ Louisville, Dept Comp Engn & Comp Sci, Louisville, KY 40292 USA
[2] Univ Louisville, Dept Chem, Louisville, KY 40292 USA
[3] Univ Louisville, Dept Med, Louisville, KY 40292 USA
[4] Univ Louisville, Dept Pharmacol Toxicol, Louisville, KY 40292 USA
[5] Univ Louisville, Multimedia Res Lab, Louisville, KY 40292 USA
[6] Univ Louisville, Bioanalyt Grp, Louisville, KY 40292 USA
[7] Louisville VAMC, Gastroenterol, Louisville, KY USA
来源
SAC '19: PROCEEDINGS OF THE 34TH ACM/SIGAPP SYMPOSIUM ON APPLIED COMPUTING | 2019年
关键词
Normalization; Robust Surface Fitting; Machine Learning; Loess Regression; Biomarker Discovery; MASS-SPECTROMETRY;
D O I
10.1145/3297280.3297284
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The paper proposes a molecule specific normalization algorithm, called MSN, which adopts a robust surface fitting strategy to minimize the molecular profile difference of a group of house-keeping molecules across samples. The house-keeping molecules are those molecules whose abundance levels were not affected by the biological treatment. The applications of the MSN method on two different datasets showed that MSN is a highly efficient normalization algorithm that yields the highest sensitivity and accuracy compared to five existing normalization algorithms
引用
收藏
页码:25 / 31
页数:7
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