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首页> 美国卫生研究院文献>Briefings in Bioinformatics >Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data
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Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data

机译：基于图和规则的学习算法：使用基因组数据全面审查其在癌症类型分类和预后中的应用

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Cancer is well recognized as a complex disease with dysregulated molecular networks or modules. Graph- and rule-based analytics have been applied extensively for cancer classification as well as prognosis using large genomic and other data over the past decade. This article provides a comprehensive review of various graph- and rule-based machine learning algorithms that have been applied to numerous genomics data to determine the cancer-specific gene modules, identify gene signature-based classifiers and carry out other related objectives of potential therapeutic value. This review focuses mainly on the methodological design and features of these algorithms to facilitate the application of these graph- and rule-based analytical approaches for cancer classification and prognosis. Based on the type of data integration, we divided all the algorithms into three categories: model-based integration, pre-processing integration and post-processing integration. Each category is further divided into four sub-categories (supervised, unsupervised, semi-supervised and survival-driven learning analyses) based on learning style. Therefore, a total of 11 categories of methods are summarized with their inputs, objectives and description, advantages and potential limitations. Next, we briefly demonstrate well-known and most recently developed algorithms for each sub-category along with salient information, such as data profiles, statistical or feature selection methods and outputs. Finally, we summarize the appropriate use and efficiency of all categories of graph- and rule mining-based learning methods when input data and specific objective are given. This review aims to help readers to select and use the appropriate algorithms for cancer classification and prognosis study.
机译：众所周知，癌症是分子网络或模块失调的复杂疾病。在过去的十年中，基于图和规则的分析已广泛用于癌症分类以及使用大基因组和其他数据进行的预后分析。本文对各种基于图和规则的机器学习算法进行了全面的综述，这些算法已应用于众多基因组数据，以确定癌症特异性基因模块，识别基于基因特征的分类器并实现潜在治疗价值的其他相关目标。这篇综述主要集中在这些算法的方法设计和特征上，以促进这些基于图和规则的分析方法在癌症分类和预后中的应用。根据数据集成的类型，我们将所有算法分为三类：基于模型的集成，预处理集成和后处理集成。根据学习风格，每个类别又分为四个子类别（有监督，无监督，半监督和生存驱动的学习分析）。因此，总结了总共11类方法及其输入，目标和描述，优点和潜在限制。接下来，我们简要介绍每个子类别的知名算法和最新开发的算法以及显着信息，例如数据配置文件，统计或特征选择方法和输出。最后，当给出输入数据和特定目标时，我们总结了所有基于图和规则挖掘的学习方法的适当使用和效率。本文旨在帮助读者选择并使用适当的算法进行癌症分类和预后研究。

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