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A highly competitive system for gene name normalization, which obtains an F-measure performance of 86.4% (precision: 87.8%, recall: 85.0%) on the BioCreAtIvE-II test set, thus being on a par with the best system on that task. GeNo tackles the complex gene normalization problem by employing a carefully crafted suite of symbolic and statistical methods, and by fully relying on publicly available software and data resources, including extensive background knowledge based on semantic profiling.
Cell line recognition
Cell line recognition and normalization system, supporting corpora and tagged documents. The aim is to create corpora that is suitable for training and evaluating machine learning systems to recognize and normalize established cell line names from text. We created two manually annotated corpora, Gellus and CLL. Gellus is suitable for the training of any machine learning systems in recognizing cell line name mentions while CLL is for evaluating the systems in recognizing the Cellosaurus cell line names.
A hybrid method integrating a machine-learning model with a pattern identification strategy to identify the individual components of each composite mention. SimConcept achieves high performance in identifying and resolving composite mentions for three key biological entities: genes (90.42% in F-measure), diseases (86.47% in F-measure), and chemicals (86.05% in F-measure). SimConcept is the first text mining tool to systematically handle many types of composite mentions. It could be useful to assist the bioconcept normalization task.
pGenN / pivot-based Gene Normalization
A gene normalization system specifically tailored for plant species. The system consists of three steps: dictionary-based gene mention detection, species assignment, and intra species normalization. This pGenN website enables user search gene normalization information by keywords, a list of PMIDs, or UniProt ACs in the database. The results (Gene names and corresponding UniProt ACs) are displayed in sortable tables with text evidence and downloadable for further research.
A system for disease mention recognition and normalization in biomedical texts. AuDis utilizes an order two conditional random fields model. To optimize the results, we customize several post-processing steps, including abbreviation resolution, consistency improvement and stopwords filtering. As the official evaluation on the chemical disease relation (CDR) task in BioCreative V, it obtained the best performance (86.46% of F-score) among 40 runs on disease normalization of the disease named entity recognition (DNER) sub task. These results suggest that AuDis is a high-performance recognition system.
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