Lane Center for Computational Biology
School of Computer Science, Carnegie Mellon University
Structured Literature Image Finder: Open Source Software
Abdul-Saboor Sheikh1, Amr Ahmed2,3,
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.
In addition to a description of the SLIF system, this technical report describes the hand-labeled datasets used for training SLIF components. These datasets, and the SLIF web application, are publicly available at http://slif.cbi.cmu.edu.