file.pdf (1.62 MB)

Structured Literature Image Finder: Parsing Text and Figures in Biomedical Literature.

Download (1.62 MB)
journal contribution
posted on 01.07.2010 by Amr Ahmed, Andrew Arnold, Luis Pedro Coelho, Joshua D. Kangas, Abdul-Saboor Sheikh, Eric P Xing, William W. Cohen, Robert Murphy

The SLIF project combines text-mining and image processing to extract structured information from biomedical literature. SLIF extracts images and their captions from published papers. The captions are automatically parsed for relevant biological entities (protein and cell type names), while the images are classified according to their type (e.g., micrograph or gel). Fluorescence microscopy images are further processed and classified according to the depicted subcellular localization. The results of this process can be queried online using either a user-friendly web-interface or an XML-based web-service. As an alternative to the targeted query paradigm, SLIF also supports browsing the collection based on latent topic models which are derived from both the annotated text and the image data. The SLIF web application, as well as labeled datasets used for training system components, is publicly available at http://slif.cbi.cmu.edu.

History

Publisher Statement

This is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published at http://dx.doi.org/10.1016/j.websem.2010.04.002

Date

01/07/2010

Exports

Exports