OGER: OntoGene’s Entity Recogniser in the BeCalm TIPS Task

We present OGER, an annotation service built on top of OntoGene’s biomedical entity recognition system, which participates in the TIPS task (technical interoperability and performance of annotation servers) of the BeCalm (biomedical annotation metaserver) challenge. The annotation server is a web application tailored to the needs of the task, using an existing biomedical entity recognition suite. The core annotation module uses a knowledge-based strategy for term matching and entity linking. The server’s architecture allows parallel processing of annotation requests for an arbitrary number of documents from mixed sources. In the discussion, we show that network latency is responsible for significant overhead in the measurement of processing time. We compare the preliminary key performance indicators with an analysis drawn from the server’s log messages. We conclude that our annotation server is ready for the upcoming phases of the TIPS task

Ranking Interactions for a Curation Task

One of the key pieces of information which biomedical text mining systems are expected to extract from the literature are interactions among different types of biomedical entities (proteins, genes, diseases, drugs, etc.). Different types of entities might be considered, for example protein-protein interactions have been extensively studied as part of the Bio Creative competitive evaluations. However, more complex interactions such as those among genes, drugs, and diseases are increasingly of interest. Different databases have been used as reference for the evaluation of extraction and ranking techniques. The aim of this paper is to describe a machine-learning based reranking approach for candidate interactions extracted from the literature. The results are evaluated using data derived from the Pharm GKB database. The importance of a good ranking is particularly evident when the results are applied to support human curators

The Birman-Schwinger Operator for a Parabolic Quantum Well in a Zero-Thickness Layer in the Presence of a Two-Dimensional Attractive Gaussian Impurity

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TX Task: Automatic detection of focus organisms in biomedical publications

In biomedical information extraction (IE), a central problem is the disambiguation of ambiguous names for domain specific entities, such as proteins, genes, etc. One important dimension of ambiguity is the organism to which the entities belong: in order to disambiguate an ambiguous entity name (e.g. a protein), it is often necessary to identify the specific organism to which it refers. In this paper we present an approach to the detection and disambiguation of the focus organism(s), i.e. the organism(s) which are the subject of the research described in scientific papers, which can then be used for the disambiguation of other entities. The results are evaluated against a gold standard derived from IntAct annotations. The evaluation suggests that the results may already be useful within a curation environment and are certainly a baseline for more complex approaches

Detection of interaction articles and experimental methods in biomedical literature

Background: This article describes the approaches taken by the OntoGene group at the University of Zurich in dealing with two tasks of the BioCreative III competition: classification of articles which contain curatable protein- protein interactions (PPI-ACT) and extraction of experimental methods (PPI-IMT). Results: Two main achievements are described in this paper: (a) a system for document classification which crucially relies on the results of an advanced pipeline of natural language processing tools; (b) a system which is capable of detecting all experimental methods mentioned in scientific literature, and listing them with a competitive ranking (AUC iP/R > 0.5). Conclusions: The results of the BioCreative III shared evaluation clearly demonstrate that significant progress has been achieved in the domain of biomedical text mining in the past few years. Our own contribution, together with the results of other participants, provides evidence that natural language processing techniques have become by now an integral part of advanced text mining approaches