Howard Hughes Medical Institute and Biology Division, California Institute of Technology, Pasadena, CA 91125, USA.
BMC Bioinformatics. 2012 Jan 26;13:16. doi: 10.1186/1471-2105-13-16.
Curation of information from bioscience literature into biological knowledge databases is a crucial way of capturing experimental information in a computable form. During the biocuration process, a critical first step is to identify from all published literature the papers that contain results for a specific data type the curator is interested in annotating. This step normally requires curators to manually examine many papers to ascertain which few contain information of interest and thus, is usually time consuming. We developed an automatic method for identifying papers containing these curation data types among a large pool of published scientific papers based on the machine learning method Support Vector Machine (SVM). This classification system is completely automatic and can be readily applied to diverse experimental data types. It has been in use in production for automatic categorization of 10 different experimental datatypes in the biocuration process at WormBase for the past two years and it is in the process of being adopted in the biocuration process at FlyBase and the Saccharomyces Genome Database (SGD). We anticipate that this method can be readily adopted by various databases in the biocuration community and thereby greatly reducing time spent on an otherwise laborious and demanding task. We also developed a simple, readily automated procedure to utilize training papers of similar data types from different bodies of literature such as C. elegans and D. melanogaster to identify papers with any of these data types for a single database. This approach has great significance because for some data types, especially those of low occurrence, a single corpus often does not have enough training papers to achieve satisfactory performance.
We successfully tested the method on ten data types from WormBase, fifteen data types from FlyBase and three data types from Mouse Genomics Informatics (MGI). It is being used in the curation work flow at WormBase for automatic association of newly published papers with ten data types including RNAi, antibody, phenotype, gene regulation, mutant allele sequence, gene expression, gene product interaction, overexpression phenotype, gene interaction, and gene structure correction.
Our methods are applicable to a variety of data types with training set containing several hundreds to a few thousand documents. It is completely automatic and, thus can be readily incorporated to different workflow at different literature-based databases. We believe that the work presented here can contribute greatly to the tremendous task of automating the important yet labor-intensive biocuration effort.
从生物科学文献中整理信息到生物知识数据库是捕获可计算形式的实验信息的关键方法。在生物整理过程中，关键的第一步是从所有已发表的文献中识别出包含整理者感兴趣的特定数据类型结果的论文。这一步通常需要整理者手动检查许多论文，以确定哪些论文包含感兴趣的信息，因此通常很耗时。我们开发了一种基于机器学习方法支持向量机（SVM）的方法，用于从大量已发表的科学论文中识别包含这些整理数据类型的论文。这种分类系统是完全自动的，可以很容易地应用于不同的实验数据类型。在过去的两年中，它已在 WormBase 的生物整理过程中用于自动分类 10 种不同的实验数据类型，并正在 FlyBase 和 Saccharomyces Genome Database（SGD）的生物整理过程中被采用。我们预计，这种方法可以很容易地被生物整理社区中的各种数据库采用，从而大大减少原本繁琐和要求高的任务所花费的时间。我们还开发了一种简单、易于自动化的程序，利用来自不同文献（如秀丽隐杆线虫和黑腹果蝇）的相似数据类型的训练论文，为单个数据库识别具有任何这些数据类型的论文。这种方法具有重要意义，因为对于某些数据类型，特别是那些出现频率较低的数据类型，单个语料库通常没有足够的训练论文来达到令人满意的性能。
我们成功地在 WormBase 的十个数据类型、FlyBase 的十五个数据类型和 Mouse Genomics Informatics（MGI）的三个数据类型上测试了该方法。它正在 WormBase 的整理工作流程中使用，用于将新发表的论文与包括 RNAi、抗体、表型、基因调控、突变等位基因序列、基因表达、基因产物相互作用、过表达表型、基因相互作用和基因结构校正在内的十种数据类型自动关联。
我们的方法适用于包含几百到几千个文档的训练集的各种数据类型。它是完全自动的，因此可以很容易地整合到不同文献数据库的不同工作流程中。我们相信，这里介绍的工作可以极大地促进自动化这一重要但劳动密集型的生物整理工作的艰巨任务。
