Systematic evaluation of supervised classifiers for fecal microbiota-based prediction of colorectal cancer.
Systematic evaluation of supervised classifiers for fecal microbiota-based prediction of colorectal cancer.
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DOI：
10.18632/oncotarget.14488
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发表时间：
2017-02-07
期刊：
影响因子：
--
通讯作者：
Fang JY
中科院分区：
文献类型：
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作者：
Ai L;Tian H;Chen Z;Chen H;Xu J;Fang JY
基于粪便微生物群预测结直肠癌（CRC）是一种很有前途的CRC无创筛查方法，但分类模型的优化仍然是一个未解决的问题。本研究的目的是系统地评估不同的监督机器学习模型在两个独立的东西方人群中预测CRC的有效性。采用454 FLX焦磷酸测序技术对141例中国人群粪便中肠道菌群结构进行了分析，并基于粪便菌群操作分类单位（OTUs）采用不同的监督分类器对CRC进行了预测。因此，贝叶斯网络和随机森林在两个群体中显示出比其他算法更高的准确率，尽管贝叶斯网络的假阴性率低于随机森林。基于肠道微生物群的预测比标准粪便潜血试验（FOBT）更准确，两种方法的结合进一步提高了预测准确性。此外，当使用未分类的OTU作为输入时，BayesDMNB文本算法在中国人群中实现了更高的准确性（AUC=0.994）。综上所述，我们的研究结果表明，贝叶斯网络分类模型结合未分类的OTU可以提供一种基于肠道微生物群组成预测CRC的准确方法。
Predicting colorectal cancer (CRC) based on fecal microbiota presents a promising method for non-invasive screening of CRC, but the optimization of classification models remains an unaddressed question. The purpose of this study was to systematically evaluate the effectiveness of different supervised machine-learning models in predicting CRC in two independent eastern and western populations. The structures of intestinal microflora in feces in Chinese population (N = 141) were determined by 454 FLX pyrosequencing, and different supervised classifiers were employed to predict CRC based on fecal microbiota operational taxonomic unit (OTUs). As a result, Bayes Net and Random Forest displayed higher accuracies than other algorithms in both populations, although Bayes Net was found with a lower false negative rate than that of Random Forest. Gut microbiota-based prediction was more accurate than the standard fecal occult blood test (FOBT), and the combination of both approaches further improved the prediction accuracy. Moreover, when unclassified OTUs were used as input, the BayesDMNB text algorithm achieved higher accuracy in the Chinese population (AUC=0.994). Taken together, our results suggest that Bayes Net classification model combined with unclassified OTUs may present an accurate method for predicting CRC based on the compositions of gut microbiota.
DOI：
10.1186/1471-2210-12-1
发表时间：
2012-03-31
期刊：
BMC pharmacology
影响因子：
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作者：
Periwal V;Kishtapuram S;Open Source Drug Discovery Consortium;Scaria V
通讯作者：
Scaria V