Learning string similarity measures for gene/protein name dictionary look-up using logistic regression

MOTIVATION: One of the bottlenecks of biomedical data integration is variation of terms. Exact string matching often fails to associate a name with its biological concept, i.e. ID or accession number in the database, due to seemingly small differences of names. Soft string matching potentially enables us to find the relevant ID by considering the similarity between the names. However, the accuracy of soft matching highly depends on the similarity measure employed. RESULTS: We used logistic regression for learning a string similarity measure from a dictionary. Experiments using several large-scale gene/protein name dictionaries showed that the logistic regression-based similarity measure outperforms existing similarity measures in dictionary look-up tasks. AVAILABILITY: A dictionary look-up system using the similarity measures described in this article is available at http://text0.mib.man.ac.uk/software/mldic/.     

Nitric Oxide Causes Glutamate Release from Brain Synaptosomes

Abstract: We determined the ability of pathological levels of nitric oxide (NO) to cause glutamate release from isolated rat brain nerve terminals using a fluorometric assay. It was found that NO (0.7 and 2 µ M ) produced (4 and 10 nmol/mg of synaptosomal protein) Ca²⁺-independent glutamate release from synaptosomes (after 1 min of exposure). Spermine/NO complex (spermine NONOate; a slow NO donor) and potassium cyanide (an inhibitor of cytochrome oxidase) also caused Ca²⁺-independent glutamate release. Preincubation of synaptosomes with 5 µ M 1 H -[1,2,4]oxadiazole[4,3- a ]quinoxalin-1-one (an inhibitor of soluble guanylyl cyclase) had no effect on NO-induced Ca²⁺-independent glutamate release. Ca²⁺-independent glutamate release produced by NO was greater in a low-oxygen medium. NO, spermine NONOate, and potassium cyanide inhibited synaptosomal respiration with a similar order of potency with respect to their ability to cause glutamate release. Because NO has been shown previously to inhibit reversibly cytochrome oxidase in competition with oxygen, our findings in this study suggest that NO (and cyanide) causes glutamate release following inhibition of mitochondrial respiration at the level of cytochrome oxidase. Thus, elevated NO production leading to mitochondrial dysfunction, glutamate release, and excitotoxicity may contribute to neuronal death in neurological diseases.     

The influence of experimental scale on estimating the predation rate of Gammarus lacustris (Crustacea: Amphipoda) on Daphnia in an alpine lake

We examined the effect of experimental scale on the predationrate of Gammarus lacustris preying on Daphnia middendorffianain 20 l cubitainers, 2800 l mesocosms and diurnal whole-lakesurveys. The predation rate differed significantly among experimentalscales. It was highest in the 20 l cubitainers, intermediatein the 2800 l mesocosms and lowest in the lake. At all experimentalscales, the predation of G.lacustris on D.middendorffiana increasedas a linear function of prey density (Type I functional response).Very different predation rates were calculated for the wholelake depending on whether D.middendorffiana densities were estimatedfrom whole-water column hauls or stratified depth hauls. Thishighlights the importance of accurately determining the spatialdistribution of prey for estimating reliable in situ predationrates. We also determined prey selection by G.lacustris. Daphniamiddendorffiana was strongly selected over the calanoid copepodHesperodiaptomus arcticus. When presented with different sizedDaphnia, G.lacustris preyed size selectively on individuals2 mm. Our results show that the predation rate of small invertebratepredators is sensitive to a wide range of experimental conditionsand needs to be corroborated at a variety of scales. We concludethat the impact of G.lacustris on zooplankton communities wouldbe strongest in small fishless lakes or ponds.     

Text mining and ontologies in biomedicine: making sense of raw text

The volume of biomedical literature is increasing at such a rate that it is becoming difficult to locate, retrieve and manage the reported information without text mining, which aims to automatically distill information, extract facts, discover implicit links and generate hypotheses relevant to user needs. Ontologies, as conceptual models, provide the necessary framework for semantic representation of textual information. The principal link between text and an ontology is terminology, which maps terms to domain-specific concepts. This paper summarises different approaches in which ontologies have been used for text-mining applications in biomedicine.     

The BioLexicon: a large-scale terminological resource for biomedical text mining

Background

Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events.

Results

This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard.

Conclusions

The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring.