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首页> 外文期刊>International journal of computational biology and drug design >Predicting tumour stages of lung cancer adenocarcinoma tumours from pooled microarray data using machine learning methods
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Predicting tumour stages of lung cancer adenocarcinoma tumours from pooled microarray data using machine learning methods

机译：使用机器学习方法从汇总的微阵列数据预测肺癌腺癌的肿瘤分期

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This paper involved a novel method combination of predicting lung cancer adenocarcinoma stages using differential expression analysis for gene selection (linear modelling) and machine learning methods (support vector machines (SVMs) and random forest) on a pooled dataset from multiple publicly available microarray experiments. The raw data of 123 tumour microarray samples were initially preprocessed and analysed using robust multi-array average (RMA) and linear models for microarray data (LIMMA) to screen a list of significantly differential expressed genes, where two gene lists were identified according to different experimental settings. These two gene lists were then placed into the SVM model and random forest (RF) model for further investigation to build the prediction models. As result, both the SVM and RF models provided a lung cancer stage prediction model with the accuracy ranging from 67% to 71 %.
机译：本文涉及从多个公开的微阵列实验收集的数据集上，使用差异表达分析进行基因选择（线性建模）和机器学习方法（支持向量机（SVM）和随机森林）预测肺癌腺癌分期的新方法组合。首先对123个肿瘤微阵列样品的原始数据进行预处理，然后使用稳健的多阵列平均数（RMA）和线性阵列微阵列数据模型（LIMMA）进行分析，以筛选出表达差异显着的基因列表，其中根据不同的基因识别出两个基因列表实验设置。然后将这两个基因列表放入SVM模型和随机森林（RF）模型中，以进行进一步研究以构建预测模型。结果，SVM和RF模型都提供了肺癌分期预测模型，其准确度范围为67％至71％。

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