Co-occurrence based meta-analysis of scientific texts: retrieving biological relationships between genes
Co-occurrence based meta-analysis of scientific texts: retrieving biological relationships between genes
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DOI：
10.1093/bioinformatics/bti268
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发表时间：
2005-05-01
期刊：
影响因子：
5.8
通讯作者：
Kors, JA
中科院分区：
文献类型：
--
作者：
Jelier, R;Jenster, G;Kors, JA
动机：分子生物学中高通量实验的出现产生了对有效提取和使用大量基因信息的方法的需求。最近，联想概念空间（ACS）已被开发用于表示从生物医学文献中提取的信息。ACS是一个欧几里德空间，其中同义词概念被定位，概念之间的距离表示它们的相关性。ACS使用概念的共现作为信息源。在本文中，我们评估如何以及该系统可以检索功能相关的基因，我们比较其性能与一个简单的基因共现method.Results：要评估的ACS的性能，我们组成了一个测试集的五组功能相关的基因。在ACS中，五组中有四组获得了良好的评分。当与基因共现方法相比时，ACS能够揭示更多的功能生物学关系，并且可以以每个基因可用的较少文献实现结果。层次聚类进行ACS的输出，作为一个潜在的帮助用户，并发现提供有用的集群。我们的研究结果表明，该算法可以为研究大量基因的研究人员的价值。
Motivation: The advent of high-throughput experiments in molecular biology creates a need for methods to efficiently extract and use information for large numbers of genes. Recently, the associative concept space (ACS) has been developed for the representation of information extracted from biomedical literature. The ACS is a Euclidean space in which thesaurus concepts are positioned and the distances between concepts indicates their relatedness. The ACS uses co-occurrence of concepts as a source of information. In this paper we evaluate how well the system can retrieve functionally related genes and we compare its performance with a simple gene co-occurrence method.Results: To assess the performance of the ACS we composed a test set of five groups of functionally related genes. With the ACS good scores were obtained for four of the five groups. When compared to the gene co-occurrence method, the ACS is capable of revealing more functional biological relations and can achieve results with less literature available per gene. Hierarchical clustering was performed on the ACS output, as a potential aid to users, and was found to provide useful clusters. Our results suggest that the algorithm can be of value for researchers studying large numbers of genes.