A method for extracting task-oriented information from biological text sources.

IF 0.2 4区生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI:10.1504/ijdmb.2015.070072

Dhanasekaran Kuttiyapillai, R Rajeswari

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引用次数: 5

Abstract

A method for information extraction which processes the unstructured data from document collection has been introduced. A dynamic programming technique adopted to find relevant genes from sequences which are longest and accurate is used for finding matching sequences and identifying effects of various factors. The proposed method could handle complex information sequences which give different meanings in different situations, eliminating irrelevant information. The text contents were pre-processed using a general-purpose method and were applied with entity tagging component. The bottom-up scanning of key-value pairs improves content finding to generate relevant sequences to the testing task. This paper highlights context-based extraction method for extracting food safety information, which is identified from articles, guideline documents and laboratory results. The graphical disease model verifies weak component through utilisation of development data set. This improves the accuracy of information retrieval in biological text analysis and reporting applications.

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一种从生物文本源中提取面向任务信息的方法。

介绍了一种对文档集合中的非结构化数据进行处理的信息提取方法。采用动态规划技术从最长和最精确的序列中寻找相关基因，寻找匹配序列，识别各种因素的影响。该方法可以处理在不同情况下具有不同含义的复杂信息序列，消除不相关信息。采用通用方法对文本内容进行预处理，并应用实体标注组件。键值对的自底向上扫描改进了内容查找，从而生成与测试任务相关的序列。本文重点介绍了基于上下文的食品安全信息提取方法，从文章、指南文件和实验室结果中识别食品安全信息。图形化疾病模型利用发展数据集对弱组分进行验证。这提高了生物文本分析和报告应用程序中信息检索的准确性。

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来源期刊

International Journal of Data Mining and Bioinformatics 生物-数学与计算生物学

CiteScore

1.00

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Mining bioinformatics data is an emerging area at the intersection between bioinformatics and data mining. The objective of IJDMB is to facilitate collaboration between data mining researchers and bioinformaticians by presenting cutting edge research topics and methodologies in the area of data mining for bioinformatics. This perspective acknowledges the inter-disciplinary nature of research in data mining and bioinformatics and provides a unified forum for researchers/practitioners/students/policy makers to share the latest research and developments in this fast growing multi-disciplinary research area.