RESUMO
Phonological feature structure is inherently multidimensional, and decades' worth of research in acoustic phonetics has documented both the complex mappings between features and associated acoustic cues as well as the prosodic modulation of these mappings. Most previous studies have focused on how the mean values of acoustic cues vary in complex ways across multiple phonological dimensions, relying on strong assumptions of statistical independence and/or homogeneity of variance across acoustic measures. The present study probes these assumptions by exploring the mapping between phonological voicing, place, and manner features and 8 acoustic cues from tokens of 14 English consonants produced in onset and coda position. Multivariate linear models exhibiting a variety of feature-cue mappings and between-cue statistical relationships were fit to this corpus of acoustic data. Model comparisons indicate that the best statistical description of the data requires pervasive interactions between features with respect to both the locations and the shapes of phonological categories. The implications of these results for work on the production and perception of phonological contrasts is discussed.
RESUMO
Efficiently mining multiple drug interactions and reactions from Adverse Event Reporting System (AERS) is a challenging problem which has not been sufficiently addressed by existing methods. To tackle this challenge, we propose a FCI-fliter approach which leverages the efforts of UMLS mapping, frequent closed itemset mining, and uninformative association identification and removal. By applying our method on AERS, we identified a large number of multiple drug interactions with reactions. By statistical analysis, we found most of the identified associations have very small p-values which suggest that they are statistically significant. Further analysis on the results shows that many multiple drug interactions and reactions are clinically interesting, and suggests that our method may be further improved with the combination of external knowledge.
RESUMO
BACKGROUND: The Unified Medical Language System (UMLS) contains many important ontologies in which terms are connected by semantic relations. For many studies on the relationships between biomedical concepts, the use of transitively associated information from ontologies and the UMLS has been shown to be effective. Although there are a few tools and methods available for extracting transitive relationships from the UMLS, they usually have major restrictions on the length of transitive relations or on the number of data sources. OBJECTIVE: Our goal was to design an efficient online platform that enables efficient studies on the conceptual relationships between any medical terms. METHODS: To overcome the restrictions of available methods and to facilitate studies on the conceptual relationships between medical terms, we developed a Web platform, onGrid, that supports efficient transitive queries and conceptual relationship studies using the UMLS. This framework uses the latest technique in converting natural language queries into UMLS concepts, performs efficient transitive queries, and visualizes the result paths. It also dynamically builds a relationship matrix for two sets of input biomedical terms. We are thus able to perform effective studies on conceptual relationships between medical terms based on their relationship matrix. RESULTS: The advantage of onGrid is that it can be applied to study any two sets of biomedical concept relations and the relations within one set of biomedical concepts. We use onGrid to study the disease-disease relationships in the Online Mendelian Inheritance in Man (OMIM). By crossvalidating our results with an external database, the Comparative Toxicogenomics Database (CTD), we demonstrated that onGrid is effective for the study of conceptual relationships between medical terms. CONCLUSIONS: onGrid is an efficient tool for querying the UMLS for transitive relations, studying the relationship between medical terms, and generating hypotheses.