Clustering Software for Biomedical Applications

用于生物医学应用的集群软件

• 批准号: 7294271
• 负责人: Yu Chen
• 金额: $ 36.46万
• 依托单位: INSIGHTFUL CORPORATION
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-07-01 至 2008-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7294271
• 关键词: Algorithms; Area; Brain; Cactaceae; Classification; Complex; Computer software; Computers; Data; Data Analyses; Data Set; Databases; Dependency; Development; Diagnosis; Diagnostic; Disease; Environment; General Hospitals; Genetic Research; Goals; Graph; Growth; Image; Imagery; Industry; Internet; Language; Magnetic Resonance Imaging; Maps; Massachusetts; Medical Imaging; Memory; Methods; Modeling; Numbers; Optics; Pattern; Phase; Process; Property; Protein Databases; Proteins; Range; Reading; Research; Research Personnel; Science; Signal Transduction; Simulate; Structure; Techniques; Testing; Time; Validation; Visual; base; data mining; drug discovery; insight; novel; repository; size; spatial relationship; tool; tool development

DESCRIPTION (provided by applicant): We propose to provide clustering software for very large databases and for categorical data. Investigators in virtually all areas of research seek to discover patterns and relationships in data. Computer intensive exploratory analysis, or data mining, is having a huge impact in science and industry (e.g. Berkhin 2002, Maitra 2002). However, the availability of software for obtaining partitions and for their visualization lags far behind the proliferation of proposed methods and the growth in size of available databases. We believe that implementing new algorithms for clustering of large datasets that may include non-numeric attributes, and visualizing cluster properties will open new opportunities for data analysis. In Phase I, we developed scalable implementations of clustering methods, including k-means and its extensions to categorical and mixed mode data, and demonstrated that we could discover things about data through a combination of clustering and visualization that neither alone could provide. Our ultimate goal in Phases II and III is to develop a modular addition to the S-PLUS language called S+CLUSTER that provides the following key features: - A suite of clustering algorithms suitable for large and possibly high-dimensional datasets that may include categorical attributes; - Extensive capabilities for visual data exploration of the results of clustering; and - Tools for validation and diagnostics facilitating objective assessment of clustering results. We intend to create software that is flexible and easy to use, and which should enable the analysis and understanding of data from a wide range of applications. Clustering or unsupervised classification has been used in genetics research, protein classification, psychiatric research, analysis of biomedical signals, segmentation of medical images, etc. The software will be part of an integrated environment for data analysis, and it will permit the customization of the clustering process, which will extend the ability of biomedical researchers to understand complex data. New insights into microarrays, epidemiological data and protein database may have high potential in drug discovery, disease diagnosis, and treatment.

描述（由申请人提供）：我们建议为非常大的数据库和分类数据提供聚类软件。几乎所有研究领域的研究人员都试图发现数据中的模式和关系。计算机密集型探索性分析或数据挖掘对科学和工业产生了巨大的影响（例如Berkhin 2002，Maitra 2002）。然而，用于获得分区及其可视化的软件的可用性远远落后于所提出的方法的扩散和可用数据库的大小的增长。我们相信，实现对可能包含非数值属性的大型数据集进行聚类的新算法，以及可视化聚类属性将为数据分析开辟新的机会。 在第一阶段，我们开发了聚类方法的可扩展实现，包括k-means及其对分类和混合模式数据的扩展，并证明了我们可以通过聚类和可视化的组合来发现数据的内容，而这两者都不能单独提供。我们在第二阶段和第三阶段的最终目标是开发一个名为S+CLUSTER的S-PLUS语言的模块化扩展，它提供以下关键功能： - 一套聚类算法，适用于可能包含分类属性的大型且可能高维的数据集; - 对聚类结果进行可视化数据探索的广泛功能;以及 - 用于验证和诊断的工具，便于对聚类结果进行客观评估。 我们打算创建灵活且易于使用的软件，并且应该能够分析和理解来自广泛应用的数据。聚类或无监督分类已被用于遗传学研究，蛋白质分类，精神病学研究，生物医学信号分析，医学图像分割等软件将成为数据分析综合环境的一部分，它将允许定制的聚类过程，这将扩大生物医学研究人员理解复杂数据的能力。对微阵列、流行病学数据和蛋白质数据库的新见解可能在药物发现、疾病诊断和治疗方面具有很大的潜力。