Applications of machine learning and high-dimensional visualization in cancer detection, diagnosis, and management
Applications of machine learning and high-dimensional visualization in cancer detection, diagnosis, and management
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DOI：
10.1196/annals.1310.020
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发表时间：
2004-01-01
期刊：
影响因子：
--
通讯作者：
Hotchkiss, J
中科院分区：
文献类型：
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作者：
McCarthy, JF;Marx, KA;Hotchkiss, J
组合化学、基因组学和蛋白质组学的最新技术进步使得生物学和化学信息的大型数据库变得可用，这些数据库有可能极大地提高我们在分子水平上对癌症生物学的理解。对癌症生物学的这种理解可能会对我们如何在临床环境中检测，诊断和管理癌症病例产生重大影响。临床肿瘤学家面临的最大挑战之一是如何从目前可用的大量原始分子数据中提取临床有用的知识。在本文中，我们将讨论如何将机器学习和高维可视化的探索性数据分析技术应用于从异构的分子数据中提取临床有用的知识。在对机器学习和可视化技术进行了介绍性概述之后，我们描述了两种专有算法（PURS和RadViz（TM）），我们发现它们在大型生物数据集的探索性分析中非常有用。接下来，我们通过三个例子来说明这些技术在癌症检测、诊断和管理中的适用性，这些技术使用三种非常不同类型的分子数据。我们首先讨论使用我们的探索性分析技术对蛋白质组质谱数据检测卵巢癌。接下来，我们讨论这些技术在基因表达数据上的诊断用途，以区分肺鳞状细胞癌和腺癌。最后，我们说明了使用这些技术在选择从数据库中的化合物，那些最有效的管理与黑色素瘤与白血病患者。
Recent technical advances in combinatorial chemistry, genomics, and proteomics have made available large databases of biological and chemical information that have the potential to dramatically improve our understanding of cancer biology at the molecular level. Such an understanding of cancer biology could have a substantial impact on how we detect, diagnose, and manage cancer cases in the clinical setting. One of the biggest challenges facing clinical oncologists is how to extract clinically useful knowledge from the overwhelming amount of raw molecular data that are currently available. In this paper, we discuss how the exploratory data analysis techniques of machine learning and high-dimensional visualization can be applied to extract clinically useful knowledge from a heterogeneous assortment of molecular data. After an introductory overview of machine learning and visualization techniques, we describe two proprietary algorithms (PURS and RadViz(TM)) that we have found to be useful in the exploratory analysis of large biological data sets. We next illustrate, by way of three examples, the, applicability of these techniques to cancer detection, diagnosis, and management using three very different types of molecular data. We first discuss the use of our exploratory analysis techniques on proteomic mass spectroscopy data for the detection of ovarian cancer. Next, we discuss the diagnostic use of these techniques on gene expression data to differentiate between squamous and adenocarcinoma of the lung. Finally, we illustrate the use of such techniques in selecting from a database of chemical compounds those most effective in managing patients with melanoma versus leukemia.