SignS: a parallelized, open-source, freely available, web-based tool for gene selection and molecular signatures for survival and censored data.
SignS: a parallelized, open-source, freely available, web-based tool for gene selection and molecular signatures for survival and censored data.
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符号：一种并行的，开源的，可自由使用的基于网络选择的工具，用于基因选择和生存和审查数据的分子特征。
DOI：
10.1186/1471-2105-9-30
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发表时间：
2008-01-21
影响因子：
3
通讯作者：
Diaz-Uriarte, Ramon
中科院分区：
文献类型：
--
作者：
Diaz-Uriarte, Ramon
在许多试图将基因表达与患者生存联系起来的微阵列研究中，审查数据越来越普遍。在过去的两年中，已经提出了几种新的方法。然而，这些方法中的大多数对生物医学研究人员来说是不可用的，这导致了许多基于生存数据的临时和次优方法的重新实现。我们开发了SignS（生存数据签名），这是一个开源的、免费的、基于网络的工具和R包，用于基因选择、构建分子签名和预测生存数据。SignS实施了四种方法，根据现有的审查，这些方法表现良好，并且由于性质不同，提供了互补的方法。我们通过MPI使用并行计算，大大减少了用户等待时间。交叉验证用于评估解决方案的预测性能和稳定性，后者是一个越来越受关注的问题，因为通常有几个具有相似预测性能的解决方案。由于模型中的基因和特征可以发送到其他免费的在线工具，以检查PubMed参考文献、GO术语以及选定基因的KEGG和Reactome途径，因此增强了结果的生物学解释。SignS是第一个基于网络的表达数据生存分析工具，也是为数不多的以生物医学研究人员为目标用户的工具之一。sign也是为数不多的广泛使用并行化的基于web的生物信息学应用程序之一，包括容错和崩溃恢复。由于它结合了实现的方法、并行计算的使用、代码的可用性以及与其他数据库的链接，sign是一个独特的工具，将与生物医学研究人员、生物统计学家和生物信息学家直接相关。
Censored data are increasingly common in many microarray studies that attempt to relate gene expression to patient survival. Several new methods have been proposed in the last two years. Most of these methods, however, are not available to biomedical researchers, leading to many re-implementations from scratch of ad-hoc, and suboptimal, approaches with survival data. We have developed SignS (Signatures for Survival data), an open-source, freely-available, web-based tool and R package for gene selection, building molecular signatures, and prediction with survival data. SignS implements four methods which, according to existing reviews, perform well and, by being of a very different nature, offer complementary approaches. We use parallel computing via MPI, leading to large decreases in user waiting time. Cross-validation is used to asses predictive performance and stability of solutions, the latter an issue of increasing concern given that there are often several solutions with similar predictive performance. Biological interpretation of results is enhanced because genes and signatures in models can be sent to other freely-available on-line tools for examination of PubMed references, GO terms, and KEGG and Reactome pathways of selected genes. SignS is the first web-based tool for survival analysis of expression data, and one of the very few with biomedical researchers as target users. SignS is also one of the few bioinformatics web-based applications to extensively use parallelization, including fault tolerance and crash recovery. Because of its combination of methods implemented, usage of parallel computing, code availability, and links to additional data bases, SignS is a unique tool, and will be of immediate relevance to biomedical researchers, biostatisticians and bioinformaticians.
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