Text Mining Point Mutations for Genetic Diagnosis Array

说明： 文本挖掘应用程序试图缓解从大量文献中识别、搜索和提取相关信息的问题。这项提议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从讨论人类遗传疾病的文献中提取点突变，并将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。首先，将使用机器学习和统计自然语言处理技术开发文本挖掘应用程序。其次，点突变挖掘应用程序将应用于与遗传疾病相关的大量文献，并将检索到的点突变存储在电子数据库中。这些点突变的集合将被检查，以发现作为遗传疾病标志的基因的多态。这些人类基因组中的多态位置将被收集并用于设计一种DMA微阵列芯片，该芯片可以对数万个单核苷酸多态进行基因分型。微阵列芯片可用于产前基因诊断，以筛查数百种遗传疾病。

DESCRIPTION: 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 DNA 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 nucleotide polymorphisms. The microarray chip can be used for prenatal genetic diagnosis purposes to screen for hundreds of genetic disorders.