Semi-supervised methods to predict patient survival from gene expression data.
Semi-supervised methods to predict patient survival from gene expression data.
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DOI：
10.1371/journal.pbio.0020108
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发表时间：
2004-04
期刊：
影响因子：
9.8
通讯作者：
Tibshirani R
中科院分区：
文献类型：
--
作者：
Bair E;Tibshirani R
DNA微阵列研究的一个重要目标是开发工具，根据肿瘤的遗传特征更准确地诊断癌症。在文献中有几种现有的技术用于进行这种类型的诊断。不幸的是，这些技术中的大多数假设已经知道存在不同的癌症亚型。当这些亚型以前没有被鉴定时，它们的效用是有限的。虽然存在用于识别此类亚型的方法，但这些方法并不适用于所有数据集。希望开发一种程序来发现适用于各种情况的此类亚型。即使不知道某种癌症的可能亚型，但通常可以获得有关患者的临床信息，例如他们的生存时间。在这项研究中，我们开发了一些程序，利用基因表达数据和临床数据来识别癌症的亚型，并利用这些知识来诊断未来的患者。这些程序已成功地应用于几个公开的数据集。我们提出的诊断程序，准确地预测未来患者的生存的基因表达谱和生存时间的基础上，以前的病人。这有可能成为诊断和治疗癌症的有力工具。利用基因表达数据和临床数据来识别癌症亚型的程序可以提供更准确的诊断
An important goal of DNA microarray research is to develop tools to diagnose cancer more accurately based on the genetic profile of a tumor. There are several existing techniques in the literature for performing this type of diagnosis. Unfortunately, most of these techniques assume that different subtypes of cancer are already known to exist. Their utility is limited when such subtypes have not been previously identified. Although methods for identifying such subtypes exist, these methods do not work well for all datasets. It would be desirable to develop a procedure to find such subtypes that is applicable in a wide variety of circumstances. Even if no information is known about possible subtypes of a certain form of cancer, clinical information about the patients, such as their survival time, is often available. In this study, we develop some procedures that utilize both the gene expression data and the clinical data to identify subtypes of cancer and use this knowledge to diagnose future patients. These procedures were successfully applied to several publicly available datasets. We present diagnostic procedures that accurately predict the survival of future patients based on the gene expression profile and survival times of previous patients. This has the potential to be a powerful tool for diagnosing and treating cancer. Procedures that utilize both gene expression data and clinical data to identify subtypes of cancer can provide more accurate prognoses
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