Automatic extraction of gene/protein biological functions from biomedical text
Automatic extraction of gene/protein biological functions from biomedical text
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DOI：
10.1093/bioinformatics/bti084
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发表时间：
2005-04-01
期刊：
影响因子：
5.8
通讯作者：
Takagi, T
中科院分区：
文献类型：
--
作者：
Koike, A;Niwa, Y;Takagi, T
动机：随着生物医学科学的快速发展和高通量分析方法的发展，从生物医学文本中提取各类信息变得至关重要。由于基因的自动功能注释是非常有用的解释大量的高通量数据有效地，从文本中的基因功能相关的信息的自动提取的需求一直在增加。结果：我们已经开发出一种方法，用于自动提取的生物过程功能的基因/蛋白质/家庭基于基因本体论（GO）从文本中使用浅解析器和句子结构分析技术。当基因/蛋白质/家族名称及其功能以ACTOR（作用者）和OBJECT（作用接受者）关系描述时，相应的GO-ID被分配给基因/蛋白质/家族。基因/蛋白质/家族的名字被识别使用基因/蛋白质/家族的名字字典由我们的小组开发。为了实现基因/蛋白质/家族功能的广泛识别，我们半自动地收集基于GO的功能术语，使用共现，搭配相似性和基于规则的技术。初步实验表明，我们的方法具有估计召回率为54-64%，精确度为91-94%的实际描述的功能摘要。当应用于PUBMED时，它提取了主要真核生物的超过190 000个基因-GO关系和150 000个家族-GO关系。
Motivation: With the rapid advancement of biomedical science and the development of high-throughput analysis methods, the extraction of various types of information from biomedical text has become critical. Since automatic functional annotations of genes are quite useful for interpreting large amounts of high-throughput data efficiently, the demand for automatic extraction of information related to gene functions from text has been increasing.Results: We have developed a method for automatically extracting the biological process functions of genes/protein/families based on Gene Ontology (GO) from text using a shallow parser and sentence structure analysis techniques. When the gene/protein/family names and their functions are described in ACTOR (doer of action) and OBJECT (receiver of action) relationships, the corresponding GO-IDs are assigned to the genes/proteins/families. The gene/protein/family names are recognized using the gene/protein/family name dictionaries developed by our group. To achieve wide recognition of the gene/protein/family functions, we semi-automatically gather functional terms based on GO using co-occurrence, collocation similarities and rule-based techniques. A preliminary experiment demonstrated that our method has an estimated recall of 54-64% with a precision of 91-94% for actually described functions in abstracts. When applied to the PUBMED, it extracted over 190 000 gene-GO relationships and 150 000 family-GO relationships for major eukaryotes.