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Overview of BioCreAtIvE: critical assessment of information extraction for biology
The first BioCreAtIvE assessment provided state-of-the-art performance results for a basic task (gene name finding and normalization), where the best systems achieved a balanced 80% precision / recall or better, which potentially makes them suitable for real applications in biology. Expand
Overview of BioCreative II gene mention recognition
It is demonstrated that, by combining the results from all submissions, an F score of 0.9066 is feasible, and furthermore that the best result makes use of the lowest scoring submissions. Expand
BABELOMICS: a systems biology perspective in the functional annotation of genome-scale experiments
A new version of Babelomics, a complete suite of web tools for functional analysis of genome-scale experiments, with new and improved tools, is presented, now more oriented to test systems biology inspired hypotheses. Expand
Automatic Extraction of Biological Information from Scientific Text: Protein-Protein Interactions
The basic design of a system for automatic detection of protein-protein interactions extracted from scientific abstracts is described and the feasibility of developing a fully automated system able to describe networks of protein interactions with sufficient accuracy is demonstrated. Expand
UvA-DARE ( Digital Academic Repository ) Overview of BioCreative II gene mention recognition
Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop. In this task participants designed systems to identify substrings in sentences corresponding to gene nameExpand
Evaluation of BioCreAtIvE assessment of task 2
Concepts provided by GO are currently the most extended set of terms used for annotating gene products, thus they were explored to assess how effectively text mining tools are able to extract those annotations automatically. Expand
The Frame-Based Module of the SUISEKI Information Extraction System
SUISEKI, an information extraction system that takes an intermediate view of the problem by requiring the two names to be in a frame that indicates a direct or indirect interaction between them, is developed. Expand
Status of text-mining techniques applied to biomedical text.
Making a computer understand human language has proven to be a complex achievement, but there are techniques capable of detecting, distinguishing and extracting a limited number of different classes of facts. Expand
Can Bibliographic Pointers for Known Biological Data Be Found Automatically? Protein Interactions as a Case Study
The DIP data set is proposed as a biological reference to benchmark IE systems for detecting previously known interactions and a positive finding is the capacity of the IE system to identify new relations between proteins, even in a set of proteins previously characterized by human experts. Expand
The potential use of SUISEKI as a protein interaction discovery tool.
A large-scale analysis for the prediction of new interactions based on the interaction network for the ones already known and detected automatically in the literature, and illustrates how interactions described in the year 2000 could have been proposed as reasonable working hypothesis with the information previously available in the automatically extracted network of interactions. Expand