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Path2Surv: Pathway/gene set-based survival analysis using multiple kernel learning.
Bioinformatics ( IF 5.8 ) Pub Date : 2019-12-15 , DOI: 10.1093/bioinformatics/btz446
Onur Dereli 1 , Ceyda Oğuz 2 , Mehmet Gönen 2, 3, 4
Affiliation  

MOTIVATION Survival analysis methods that integrate pathways/gene sets into their learning model could identify molecular mechanisms that determine survival characteristics of patients. Rather than first picking the predictive pathways/gene sets from a given collection and then training a predictive model on the subset of genomic features mapped to these selected pathways/gene sets, we developed a novel machine learning algorithm (Path2Surv) that conjointly performs these two steps using multiple kernel learning. RESULTS We extensively tested our Path2Surv algorithm on 7655 patients from 20 cancer types using cancer-specific pathway/gene set collections and gene expression profiles of these patients. Path2Surv statistically significantly outperformed survival random forest (RF) on 12 out of 20 datasets and obtained comparable predictive performance against survival support vector machine (SVM) using significantly fewer gene expression features (i.e. less than 10% of what survival RF and survival SVM used). AVAILABILITY AND IMPLEMENTATION Our implementations of survival SVM and Path2Surv algorithms in R are available at https://github.com/mehmetgonen/path2surv together with the scripts that replicate the reported experiments. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

中文翻译:

Path2Surv:使用多核学习的基于路径/基因集的生存分析。

动机将途径/基因组整合到他们的学习模型中的生存​​分析方法可以确定确定患者生存特征的分子机制。我们没有先从给定的集合中选择预测途径/基因集,然后在映射到这些选定途径/基因集的基因组特征子集上训练预测模型,而是开发了一种新颖的机器学习算法(Path2Surv),该算法联合执行这两个使用多个内核学习的步骤。结果我们使用癌症特异性途径/基因组集合和这些患者的基因表达谱,对来自20种癌症的7655名患者进行了广泛的Path2Surv算法测试。在20个数据集中的12个数据集上,Path2Surv在统计上明显胜过生存随机森林(RF),并且使用明显更少的基因表达特征(即,不到所使用的生存RF和生存SVM的10%)获得了与生存支持向量机(SVM)相当的预测性能。可用性和实现我们可以在https://github.com/mehmetgonen/path2surv中获得我们在R中的生存SVM和Path2Surv算法的实现,以及用于复制所报告实验的脚本。补充信息补充数据可从Bioinformatics在线获得。可用性和实现我们可以在https://github.com/mehmetgonen/path2surv中获得R中的生存SVM和Path2Surv算法的实现,以及用于复制所报告实验的脚本。补充信息补充数据可从Bioinformatics在线获得。可用性和实现我们可以在https://github.com/mehmetgonen/path2surv中获得我们在R中的生存SVM和Path2Surv算法的实现,以及用于复制所报告实验的脚本。补充信息补充数据可从Bioinformatics在线获得。
更新日期:2020-01-13
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