利用基因表达的meta分析对骨肉瘤进行分类

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI:10.1109/BIBM.2018.8621119

Olivia Alge, Jonathan Gryak, Yi-Yang Hua, K. Najarian

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引用次数: 1

摘要

基因表达的荟萃分析提供了比较不同平台上基因表达的机会。在本文中，我们使用上海交通大学团队收集的RNA-seq数据和公开的微阵列数据进行meta分析，构建随机森林分类模型。随机森林模型在训练集中交叉验证的平均准确率为74.1%，在测试集中交叉验证的平均准确率为80.0%。

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Classifying Osteosarcoma Using Meta-Analysis of Gene Expression

Meta-analysis of gene expression provides the opportunity to compare gene expression across different platforms. In this paper, we use a meta-analysis of RNA-seq data collected by the SJTU team and publicly available microarray data to build a Random Forest classification model. The Random Forest model had average accuracy of 74.1% for cross-validation in the training set and achieved accuracy of 80.0% on the testing set.

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2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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