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Logo of bmcbioiBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Bioinformatics
BMC Bioinformatics. 2012; 13: 16.
Published online Jan 26, 2012. doi:  10.1186/1471-2105-13-16
PMCID: PMC3305665
Automatic categorization of diverse experimental information in the bioscience literature
Ruihua Fang,1 Gary Schindelman,1 Kimberly Van Auken,1 Jolene Fernandes,1 Wen Chen,1 Xiaodong Wang,1 Paul Davis,2 Mary Ann Tuli,2 Steven J Marygold,3 Gillian Millburn,3 Beverley Matthews,4 Haiyan Zhang,4 Nick Brown,5 William M Gelbart,4 and Paul W Sternbergcorresponding author1
1Howard Hughes Medical Institute and Biology Division, California Institute of Technology, Pasadena, CA 91125, USA
2Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK
3Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK
4Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
5The Gurdon Institute and Department of Physiology, Development & Neuroscience, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, UK
corresponding authorCorresponding author.
Ruihua Fang: rfang/at/; Gary Schindelman: garys/at/; Kimberly Van Auken: vanauken/at/; Jolene Fernandes: jolenef/at/; Wen Chen: wchen/at/; Xiaodong Wang: xdwang/at/; Paul Davis: paul.davis/at/; Mary Ann Tuli: mt3/at/; Steven J Marygold: sjm4/at/; Gillian Millburn: gm119/at/; Beverley Matthews: bmatthew/at/; Haiyan Zhang: haiyan/at/; Nick Brown: n.brown/at/; William M Gelbart: gelbart/at/; Paul W Sternberg: pws/at/
Received October 5, 2011; Accepted January 26, 2012.
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.
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