Spasic Irena, Simeonidis Evangelos, Messiha Hanan L, Paton Norman W, Kell Douglas B
Manchester Centre for Integrative Systems Biology, The University of Manchester, Manchester, UK.
Bioinformatics. 2009 Jun 1;25(11):1404-11. doi: 10.1093/bioinformatics/btp175. Epub 2009 Mar 31.
Most experimental evidence on kinetic parameters is buried in the literature, whose manual searching is complex, time consuming and partial. These shortcomings become particularly acute in systems biology, where these parameters need to be integrated into detailed, genome-scale, metabolic models. These problems are addressed by KiPar, a dedicated information retrieval system designed to facilitate access to the literature relevant for kinetic modelling of a given metabolic pathway in yeast. Searching for kinetic data in the context of an individual pathway offers modularity as a way of tackling the complexity of developing a full metabolic model. It is also suitable for large-scale mining, since multiple reactions and their kinetic parameters can be specified in a single search request, rather than one reaction at a time, which is unsuitable given the size of genome-scale models.
We developed an integrative approach, combining public data and software resources for the rapid development of large-scale text mining tools targeting complex biological information. The user supplies input in the form of identifiers used in relevant data resources to refer to the concepts of interest, e.g. EC numbers, GO and SBO identifiers. By doing so, the user is freed from providing any other knowledge or terminology concerned with these concepts and their relations, since they are retrieved from these and cross-referenced resources automatically. The terminology acquired is used to index the literature by mapping concepts to their synonyms, and then to textual documents mentioning them. The indexing results and the previously acquired knowledge about relations between concepts are used to formulate complex search queries aiming at documents relevant to the user's information needs. The conceptual approach is demonstrated in the implementation of KiPar. Evaluation reveals that KiPar performs better than a Boolean search. The precision achieved for abstracts (60%) and full-text articles (48%) is considerably better than the baseline precision (44% and 24%, respectively). The baseline recall is improved by 36% for abstracts and by 100% for full text. It appears that full-text articles are a much richer source of information on kinetic data than are their abstracts. Finally, the combined results for abstracts and full text compared with the curated literature provide high values for relative recall (88%) and novelty ratio (92%), suggesting that the system is able to retrieve a high proportion of new documents.
Source code and documentation are available at: (http://www.mcisb.org/resources/kipar/).
大多数关于动力学参数的实验证据都埋没在文献中，人工检索这些文献复杂、耗时且不全面。在系统生物学中，这些缺点尤为突出，因为需要将这些参数整合到详细的、全基因组规模的代谢模型中。KiPar解决了这些问题，它是一个专门的信息检索系统，旨在方便获取与酵母中给定代谢途径动力学建模相关的文献。在单个途径的背景下搜索动力学数据提供了模块化，作为应对开发完整代谢模型复杂性的一种方式。它也适用于大规模挖掘，因为可以在单个搜索请求中指定多个反应及其动力学参数，而不是一次指定一个反应，鉴于全基因组规模模型的大小，一次指定一个反应是不合适的。
我们开发了一种综合方法，结合公共数据和软件资源，用于快速开发针对复杂生物信息的大规模文本挖掘工具。用户以相关数据资源中使用的标识符的形式提供输入，以指代感兴趣的概念，例如酶委员会编号、基因本体（GO）和系统生物学本体（SBO）标识符。通过这样做，用户无需提供与这些概念及其关系相关的任何其他知识或术语，因为它们会自动从这些资源和交叉引用的资源中检索出来。获取的术语用于通过将概念映射到其同义词，然后映射到提及它们的文本文档来对文献进行索引。索引结果以及先前获取的关于概念之间关系的知识用于制定针对与用户信息需求相关的文档的复杂搜索查询。KiPar的实现展示了这种概念方法。评估表明，KiPar的性能优于布尔搜索。摘要（60%）和全文文章（48%）的精确率明显高于基线精确率（分别为44%和24%）。摘要的基线召回率提高了36%，全文的基线召回率提高了100%。似乎全文文章是关于动力学数据的比其摘要丰富得多的信息来源。最后，与经过整理的文献相比，摘要和全文的综合结果提供了较高的相对召回率（88%）和新颖率（92%），表明该系统能够检索出高比例的新文档。
源代码和文档可在以下网址获取：(http://www.mcisb.org/resources/kipar/) 。
