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We introduce knowledge discovery for vasculitic diseases in this paper. Vasculitic diseases affect some organs and tissues and diagnosing can be quite difficult. Biomedical literature can contain hidden and useful knowledge for biomedical research and we develop a study based on co-occurrence analysis by using the articles in MEDLINE which is a widely used database.The mostly seen vasculitic diseases are selected to explore hidden patterns. We select PolySearch system as a web based biomedical text mining tool to find organs and tissues in the articles and create two separate datasets with their frequencies for each disease. After forming these datasets, we apply hierarchical clustering analysis to find similarities between the diseases. Clustering analysis reveals some similarities between diseases. We think that the results of clustered diseases positively affect on the medical research of vasculitic diseases especially during the diagnosis and certain similarities can provide different views to medical specialists. [ABSTRACT FROM AUTHOR]

Most of the global corpus of medicinal chemistry data is only published in patents. However, extracting this from patent documents and subsequent integration with literature and database sources poses unique challenges. This work presents the investigation of an extensive full-text patent resource, including automated name-to-chemical structure conversion, licensed by AstraZeneca via a consortium arrangement with IBM. Our initial focus was identifying protein targets in patent titles linked to extracted bioactive compounds. We benchmarked target recognition strategies against target-assay-compound relationships manually curated from patents by GVKBIO. By analysis of word frequencies and protein names we assessed the false-negative problem of targets not specified in titles and false-positives from non-target proteins in titles. We also examined the time-signals for selected target and non-target names by year of patent publication. Our results exemplify problems and some solutions for extracting data from this source. [ABSTRACT FROM AUTHOR]

Gene/protein recognition and normalization are important prerequisite steps for many biological text mining tasks. Even if great efforts have been dedicated to these problems and effective solutions have been reported, the availability of easily integrated tools to perform these tasks is still deficient. We therefore propose Moara, a Java library that implements gene/protein recognition and normalization steps based on machine learning approaches. The system may be trained with extra documents for the recognition procedure and new organism may be added in the normalization step. The novelty of the methodology used in Moara lies in the design of a system that is not tailored to a specific organism and therefore does not need any organism-dependent tuning in the algorithms and in the dictionaries it uses. Moara can be used either as a standalone application or incorporated in a text mining system and it is available at: http://moara.dacya.ucm.es [ABSTRACT FROM AUTHOR]

We aim to design a system for classifying scientific articles based on the presence of protein characterization experiments, intending to aid the curators populating JCVI΄s Characterized Protein (CHAR) Database of experimentally characterized proteins. We trained two classifiers using small datasets labeled by CHAR curators, and another classifier based on a much larger dataset using annotations from public databases. Performance varied greatly, in ways we did not anticipate. We describe the datasets, the classification method, and discuss the unexpected results. [ABSTRACT FROM AUTHOR]