Text Mining Point Mutations for Genetic Diagnosis Array

文本挖掘应用程序试图减轻识别，搜索和提取相关信息的问题， 大量文献中的信息。本提案的目标是创建一个文本挖掘应用程序， 从生物医学文献中提取蛋白质点突变。开发的应用程序将用于提取 讨论人类遗传疾病的文献中的点突变，以及检索到的点突变数据库 用于设计用于产前基因诊断的DNA微阵列芯片的突变。一、文本挖掘 将使用机器学习和统计自然语言处理技术开发应用程序。 第二，将点突变挖掘应用于遗传相关的大量文献集 疾病和检索到的点突变储存在电子数据库中。此集合点 将检查突变以发现作为遗传病标记的基因中的多态性。这些 人类基因组中的多态性位置将被收集并用于设计DMA微阵列芯片， 可以对成千上万的单核苷酸多态性进行基因分型。该微阵列芯片可用于 产前基因诊断的目的是筛查数百种遗传疾病。

Text mining applications seek to alleviate the problems with identifying, searching, and extracting relevant information from large sets of literature. The goal of this proposal is to create a text mining application to extract protein point mutations from biomedical literature. The developed application will be used to extract point mutations from literature discussing human genetic disorders, and the retrieved database of point mutations used to design a DMA microarray chip for prenatal genetic diagnosis. First, the text mining application will be developed using machine learning and statistical natural language processing techniques. Second, the point mutation mining application will be applied to a large set of literature related to genetic disorders, and the retrieved point mutations deposited in an electronic database. This collection of point mutations will be examined to find polymorphisms in genes that are markers for genetic disorders. These polymorphic positions in the human genome will be gathered and used to design a DMA microarray chip that can genotype tens of thousands of single nucelotide polymorphisms. The microarray chip can be used for prenatal genetic diagnosis purposes to screen for hundreds of genetic disorders.