Automatic categorization of diverse experimental information in the bioscience literature.

ABSTRACT:

Background

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 automati

SUBMITTER: Fang R 

PROVIDER: S-EPMC3305665 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature