摘要翻译:
我们的工作涉及阐明癌症(epi)基因组、转录组和蛋白质组,以更好地理解癌细胞的分子状态及其对抗癌治疗的反应之间复杂的相互作用。为了研究这一问题,我们以前重点研究了数据仓库技术和统计数据集成。在本文中,我们介绍了最近在使用语义Web技术扩展我们的分析能力方面所做的工作。这里介绍的一个关键的新组件是现有数据仓库的SPARQL端点。该端点允许将观察到的定量数据与来自语义知识源如基因本体(GO)的现有数据合并。我们展示了如何使用语义Web工具以通用的方式集成和访问这些杂乱的、定量的和功能性的数据。我们还演示了如何使用描述逻辑(DL)推理来从现有的知识库中推断出以前未陈述的结论。作为概念的证明,我们说明了我们的设置回答关于癌细胞对去甲基化剂地西他滨耐药性的复杂问题的能力。
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英文标题:
《Analysis Of Cancer Omics Data In A Semantic Web Framework》
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作者:
Matt Holford, James McCusker, Kei Cheung and Michael Krauthammer
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最新提交年份:
2010
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类:Computer Science 计算机科学
二级分类:Computational Engineering, Finance, and Science 计算工程、金融和科学
分类描述:Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
涵盖了计算机科学在科学、工程和金融领域复杂系统的数学建模中的应用。这里的论文是跨学科和面向应用的,集中在技术和工具,使挑战性的计算模拟能够执行,其中往往需要使用超级计算机或分布式计算平台。包括ACM学科课程J.2、J.3和J.4(经济学)中的材料。
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英文摘要:
Our work concerns the elucidation of the cancer (epi)genome, transcriptome and proteome to better understand the complex interplay between a cancer cell's molecular state and its response to anti-cancer therapy. To study the problem, we have previously focused on data warehousing technologies and statistical data integration. In this paper, we present recent work on extending our analytical capabilities using Semantic Web technology. A key new component presented here is a SPARQL endpoint to our existing data warehouse. This endpoint allows the merging of observed quantitative data with existing data from semantic knowledge sources such as Gene Ontology (GO). We show how such variegated quantitative and functional data can be integrated and accessed in a universal manner using Semantic Web tools. We also demonstrate how Description Logic (DL) reasoning can be used to infer previously unstated conclusions from existing knowledge bases. As proof of concept, we illustrate the ability of our setup to answer complex queries on resistance of cancer cells to Decitabine, a demethylating agent.
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PDF链接:
https://arxiv.org/pdf/1012.1648