Evaluating gold standard corpora against gene/protein tagging solutions and lexical resources

Motivation The identification of protein and gene names (PGNs) from the scientific literature requires semantic resources: Terminological and lexical resources deliver the term candidates into PGN tagging solutions and the gold standard corpora (GSC) train them to identify term parameters and contextual features.Ideally all three resources, i.e.~corpora, lexica and taggers, cover the same domain knowledge, and thus support identification of the same types of PGNs and cover all of them.Unfortunately, none of the three serves as a predominant standard and for this reason it is worth exploring, how these three resources comply with each other.We systematically compare different PGN taggers against publicly available corpora and analyze the impact of the included lexical resource in their performance.In particular, we determine the performance gains through false positive filtering, which contributes to the disambiguation of identified PGNs. RESULTS: In general, machine learning approaches (ML-Tag) for PGN tagging show higher F1-measureperformance against the BioCreative-II and Jnlpba GSCs (exact matching), whereas the lexicon basedapproaches (LexTag) in combination with disambiguation methods show better results on FsuPrgeand PennBio. The ML-Tag solutions balance precision and recall, whereas the LexTag solutions havedifferent precision and recall profiles at the same F1-measure across all corpora. Higher recall isachieved with larger lexical resources, which also introduce more noise (false positive results). TheML-Tag solutions certainly perform best, if the test corpus is from the same GSC as the trainingcorpus. As expected, the false negative errors characterize the test corpora and - on the other hand- the profiles of the false positive mistakes characterize the tagging solutions. Lex-Tag solutions thatare based on a large terminological resource in combination with false positive filtering produce betterresults, which, in addition, provide concept identifiers from a knowledge source in contrast to ML-Tagsolutions. CONCLUSION: The standard ML-Tag solutions achieve high performance, but not across all corpora, and thus shouldbe trained using several different corpora to reduce possible biases. The LexTag solutions havedifferent profiles for their precision and recall performance, but with similar F1-measure. This resultis surprising and suggests that they cover a portion of the most common naming standards, but copedifferently with the term variability across the corpora. The false positive filtering applied to LexTagsolutions does improve the results by increasing their precision without compromising significantlytheir recall. The harmonisation of the annotation schemes in combination with standardized lexicalresources in the tagging solutions will enable their comparability and will pave the way for a sharedstandard

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Deduktiv unterstützte Rekonstruktion biologischer Netzwerke aus flexibel analysierten Textdaten

Wallmeyer T. Deduktiv unterstützte Rekonstruktion biologischer Netzwerke aus flexibel analysierten Textdaten. Bielefeld: Universität Bielefeld; 2016

PCorral - interactive mining of protein interactions from MEDLINE

The extraction of information from the scientific literature is a complex task-for researchers doing manual curation and for automatic text processing solutions. The identification of protein-protein interactions (PPIs) requires the extraction of protein named entities and their relations. Semi-automatic interactive support is one approach to combine both solutions for efficient working processes to generate reliable database content. In principle, the extraction of PPIs can be achieved with different methods that can be combined to deliver high precision and/or high recall results in different combinations at the same time. Interactive use can be achieved, if the analytical methods are fast enough to process the retrieved documents. PCorral provides interactive mining of PPIs from the scientific literature allowing curators to skim MEDLINE for PPIs at low overheads. The keyword query to PCorral steers the selection of documents, and the subsequent text analysis generates high recall and high precision results for the curator. The underlying components of PCorral process the documents on-the-fly and are available, as well, as web service from the Whatizit infrastructure. The human interface summarizes the identified PPI results, and the involved entities are linked to relevant resources and databases. Altogether, PCorral serves curator at both the beginning and the end of the curation workflow for information retrieval and information extraction. Database URL: http://www.ebi.ac.uk/Rebholz-srv/pcorral