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Deep Neural Networks (DNN), in particular, Convolutional Neural Networks (CNN), has recently achieved state-of-art results for the task of Drug-Drug Interaction (DDI) extraction. Most CNN architectures incorporate a pooling layer to reduce the dimensionality of the convolution layer output, preserving relevant features and removing irrelevant details. All the previous CNN based systems for DDI extraction used max-pooling layers.
Pubmed ID: 29897318
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MEDLINE (Medical Literature Analysis and Retrieval System Online) is the U.S. National Library of Medicine''s (NLM) premier bibliographic database that contains over 16 million references to journal articles in life sciences with a concentration on biomedicine. MEDLINE is the primary component of PubMed, part of the Entrez series of databases provided by NLM''s National Center for Biotechnology Information (NCBI). MEDLINE may also be searched via the NLM Gateway. Currently, citations from approximately 5,200 worldwide journals in 37 languages; 60 languages for older journals. Citations for MEDLINE are created by the NLM, international partners, and collaborating organizations. The subject scope of MEDLINE is biomedicine and health, broadly defined to encompass those areas of the life sciences, behavioral sciences, chemical sciences, and bioengineering needed by health professionals and others engaged in basic research and clinical care, public health, health policy development, or related educational activities. MEDLINE also covers life sciences vital to biomedical practitioners, researchers, and educators, including aspects of biology, environmental science, marine biology, plant and animal science as well as biophysics and chemistry. Sponsors: Services/products providing access to MEDLINE data are also developed and made available by organizations that lease the database from NLM.
View all literature mentionsbrat is a free, open-source, web-based tool for text annotation, visualisation and editing. brat is designed in particular for structured annotation, where the notes are not freeform text but have a fixed form that can be automatically processed and interpreted by a computer. brat is built entirely on standard web technologies, and it is not necessary to install any local software or browser plugins to use it. An annotator can set up and start using brat simply by entering the address of the brat installation into the address bar of a browser. (Setting up an entirely new brat server does require some action, but can be done in just five minutes on any system running a web server.) brat is fully configurable and can support a wide variety of annotation tasks, including, for example: * entity mention (named entity) annotation * binary relation annotation * dependency syntactic annotation * structured, n-ary event annotation * attribute/meta-knowledge annotation (e.g. negation, speculation, etc.) The tool also provides annotation support features such as text and annotation search with detailed constraints, keyword-in-context concordancing, and integrated configurable checking of task-specific semantic constraints. Annotations created in brat can be exported with a few clicks from the interface in a simple standoff format that can be easily analyzed, processed, and converted into other formats. Visualizations can be similarly be exported in their native SVG format, rendered as a bitmap (PNG format), or converted into other vector formats for embedding into documents (PDF or EPS). brat is developed as a collaborative effort between several research groups as an open source project (MIT license), and we warmly welcome contributions and participation from the community, including feature requests. We hope this tool will prove valuable to the natural language processing community, and will gladly answer questions and welcome any feedback.
View all literature mentionsBioinformatics and cheminformatics database that combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. sequence, structure, and pathway) information.
View all literature mentionsWelcome to the Neuroscience Information Framework Project, designed to serve the biomedical research community. NIF maintains the largest searchable collection of neuroscience data, the largest catalog of biomedical resources, and the largest ontology for neuroscience on the web. We welcome all feedback and suggestions and are actively looking for resource providers to make their resources accessible through NIF. Learn about the tools available to help you share your data and discover a dynamic inventory of Web-based neuroscience resources.
