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Multi-TGDR, a multi-class regularization method, identifies the metabolic profiles of hepatocellular carcinoma and cirrhosis infected with hepatitis B or hepatitis C virus 期刊论文

编号：

f40b8c2e-a910-49e3-b108-6804d3f2f38c
作者：

Tian, Suyan(田肃岩)#*^[1]Chang, Howard H.^[3]Wang, Chi^[4,5]Jiang, Jing(姜晶)^[1]Wang, Xiaomei(王晓美)^[2]Niu, Junqi(牛俊奇)*^[2]
语种：

英文
期刊：

BMC BIOINFORMATICS ISSN：1471-2105 2014 年 15 卷 ; APR 4
收录：
关键词：

Threshold gradient descent regularization (TGDR) Multi-class classification Metabolic profile Hepatocellular carcinoma (HCC) Feature selection Metabolomics Omics data
摘要：

Background: Over the last decade, metabolomics has evolved into a mainstream enterprise utilized by many laboratories globally. Like other "omics" data, metabolomics data has the characteristics of a smaller sample size compared to the number of features evaluated. Thus the selection of an optimal subset of features with a supervised classifier is imperative. We extended an existing feature selection algorithm, threshold gradient descent regularization (TGDR), to handle multi-class classification of "omics" data, and proposed two such extensions referred to as multi-TGDR. Both multi-TGDR frameworks were used to analyze a metabolomics dataset that compares the metabolic profiles of hepatocellular carcinoma (HCC) infected with hepatitis B (HBV) or C virus (HCV) with that of cirrhosis induced by HBV/HCV infection; the goal was to improve early-stage diagnosis of HCC.
Results: We applied two multi-TGDR frameworks to the HCC metabolomics data that determined TGDR thresholds either globally across classes, or locally for each class. Multi-TGDR global model selected 45 metabolites with a 0% misclassification rate (the error rate on the training data) and had a 3.82% 5-fold cross-validation (CV-5) predictive error rate. Multi-TGDR local selected 48 metabolites with a 0% misclassification rate and a 5.34% CV-5 error rate.
Conclusions: One important advantage of multi-TGDR local is that it allows inference for determining which feature is related specifically to the class/classes. Thus, we recommend multi-TGDR local be used because it has similar predictive performance and requires the same computing time as multi-TGDR global, but may provide class-specific inference.
推荐引用方式
GB/T 7714：

Tian Suyan,Chang Howard H.,Wang Chi, et al. Multi-TGDR, a multi-class regularization method, identifies the metabolic profiles of hepatocellular carcinoma and cirrhosis infected with hepatitis B or hepatitis C virus [J].BMC BIOINFORMATICS,2014,15.
APA：

Tian Suyan,Chang Howard H.,Wang Chi,Jiang Jing,&Niu Junqi.(2014).Multi-TGDR, a multi-class regularization method, identifies the metabolic profiles of hepatocellular carcinoma and cirrhosis infected with hepatitis B or hepatitis C virus .BMC BIOINFORMATICS,15.
MLA：

Tian Suyan, et al. "Multi-TGDR, a multi-class regularization method, identifies the metabolic profiles of hepatocellular carcinoma and cirrhosis infected with hepatitis B or hepatitis C virus" .BMC BIOINFORMATICS 15(2014).
入库时间：

12/16/2019 3:19:01 PM
更新时间：

12/16/2019 3:19:01 PM
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