Ontology-Driven Methods for Knowledge Acquisition and Knowledge Discovery

本体驱动的知识获取和知识发现方法

• 批准号: 8714053
• 负责人: XINGHUA LU
• 金额: $ 30.75万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8714053
• 关键词: Accounting; Achievement; Address; Algorithms; Area; Biological; Biological Process; Biomedical Research; Computing Methodologies; Controlled Vocabulary; Data; Disease; Gene Proteins; Genes; Goals; Knowledge; Knowledge Discovery; Knowledge acquisition; Learning; Literature; Malignant Neoplasms; Manuals; Maps; Methodology; Methods; Mining; Modeling; Names; Natural Language Processing; Ontology; Procedures; Process; Proteins; Psyche structure; Research; Science; Semantics; Structure; System; Systems Biology; Testing; Text; The Cancer Genome Atlas; Training; Tweens; Yeasts; biological systems; biomedical informatics; cancer cell; data mining; design; insight; interest; knowledge of results; novel; protein function; protein protein interaction; research study; text searching

DESCRIPTION (provided by applicant): A great challenge in the biomedical informatics domain is to develop computational methods that combine existing knowledge and experimental data to derive new knowledge regarding biological systems and disease mechanisms. Most knowledge regarding genes and proteins in biomedical literature is stored in the form of free text that is not suitable for computation, and the manual processes of encoding this body of knowledge into computable form cannot keep up with the rate of knowledge accumulation. The main thrust of the proposed research is to design novel statistical text-mining algorithms to acquire and represent knowledge regarding genes and proteins from free-text literature, and further to combine this acquired knowledge with experimental data to derive new knowledge. We will organize the proposed research to the following specific aims. Specific Aim 1. Develop ontology-guided semantic modeling algorithms for extracting biological concepts from free text, in which we will design hierarchical probabilistic topic models that are capable of representing biological concepts as a hierarchy and develop novel learning algorithms to infer biological concepts from free-text documents. Specific Aim 2. Integrate semantic modeling with BioNLP to extract textual evidence supporting protein-function annotations. We will develop information extraction algorithms that will combine the results of hierarchical semantic analysis and BioNLP to identify the text regions that will most likely provide evidence regarding the function of genes/proteins and map the extracted information to a controlled vocabulary. Specific Aim 3. Develop a framework to unify the procedures of knowledge reasoning and data mining for knowledge discovery. In this aim, we will reason using existing knowledge (represented in the form of an ontology) to reveal functional modules among the genes from the experimental data. We will then further develop algorithms that will reveal relationships between these gene modules by mining system-scaled experimental data. The overall framework will integrate functional reasoning and data mining in an iterative manner to refine the knowledge progressively and to derive rules such as: when genes involved in biological process X are perturbed, genes involved in biological process Y will respond. We will test the framework on the data from yeast-system biology studies and the Cancer Genome Atlas (TCGA) project to gain insights into the cellular systems and disease mechanisms of cancer cells.

描述（由申请人提供）： 生物医学信息学领域的一个巨大挑战是开发将现有知识和实验数据相结合的计算方法，以获得有关生物系统和疾病机制的新知识。生物医学文献中关于基因和蛋白质的大多数知识都以不适合计算的自由文本形式存储，并且将这些知识编码为可计算形式的手动过程无法跟上知识积累的速度。该研究的主旨是设计新的统计文本挖掘算法，以获取和表示有关基因和蛋白质的知识，从自由文本文献，并进一步联合收割机，这与实验数据获得新的知识。我们将按照以下具体目标组织拟议的研究。具体目标1.开发本体指导的语义建模算法，从自由文本中提取生物概念，其中我们将设计分层概率主题模型，能够将生物概念表示为层次结构，并开发新的学习算法，从自由文本文档中推断生物概念。具体目标2。将语义建模与BioNLP集成，以提取支持蛋白质功能注释的文本证据。我们将开发信息提取算法，该算法将联合收割机层次语义分析和BioNLP的结果，以识别最有可能提供有关基因/蛋白质功能的证据的文本区域，并将提取的信息映射到受控词汇表。具体目标3。开发一个框架来统一知识推理和知识发现的数据挖掘过程。在这个目标中，我们将使用现有的知识（以本体论的形式表示），从实验数据中揭示基因之间的功能模块。然后，我们将进一步开发算法，通过挖掘系统规模的实验数据来揭示这些基因模块之间的关系。整个框架将以迭代的方式集成功能推理和数据挖掘，以逐步完善知识并导出规则，例如：当生物过程X中涉及的基因受到干扰时，生物过程Y中涉及的基因将做出响应。我们将在酵母系统生物学研究和癌症基因组图谱（TCGA）项目的数据上测试该框架，以深入了解癌细胞的细胞系统和疾病机制。