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10.1093/database/bax019 http://scihub22266oqcxt.onion/10.1093/database/bax019 C5467573!5467573!28605776     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Database+(Oxford) 2017 ; 2017 (ä): ä Nephropedia Template TP {{tp|p=28605776|t=2017. Exploring convolutional neural networks for drug?drug interaction extraction |pdf=|usr=}}{{28605776}} gab.com Text Exploring convolutional neural networks for drug drug interaction extraction JO: Database (Oxford) http://www.kidney.de/pget.php?&tw=1&pmid=28605776 #FOAvip Drug?drug interaction (DDI), which is a specific type of adverse drug reaction, occurs when a drug influences the level or activity of another drug. Natural language processing techniques can provide health-care professionals with a novel way of reducing the time spent reviewing the literature for potential DDIs. The current state-of-the-art for the extraction of DDIs is based on feature-engineering algorithms (such as support vector machines), which usually require considerable time and effort. One possible alternative to these approaches includes deep learning. This technique aims to automatically learn the best feature representation from the input data for a given task. The purpose of this paper is to examine whether a convolutional neural network (CNN), which only uses word embeddings as input features, can be applied successfully to classify DDIs from biomedical texts. Proposed herein, is a CNN architecture with only one hidden layer, thus making the model more computationally efficient, and we perform detailed experiments in order to determine the best settings of the model. The goal is to determine the best parameter of this basic CNN that should be considered for future research. The experimental results show that the proposed approach is promising because it attained the second position in the 2013 rankings of the DDI extraction challenge. However, it obtained worse results than previous works using neural networks with more complex architectures. Twit Text FOAVip Exploring convolutional neural networks for drug drug interaction extraction ????Database (Oxford) http://www.kidney.de/pget.php?&tw=1&pmid=28605776 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28605776 #FOAvip English Wikipedia <ref>{{Cite pmid|28605776}}</ref> Exploring convolutional neural networks for drug?drug interaction extraction #MMPMID28605776 Suárez-Paniagua V; Segura-Bedmar I; Martínez P Database (Oxford) 2017[]; 2017 (ä): ä PMID28605776show ga Drug?drug interaction (DDI), which is a specific type of adverse drug reaction, occurs when a drug influences the level or activity of another drug. Natural language processing techniques can provide health-care professionals with a novel way of reducing the time spent reviewing the literature for potential DDIs. The current state-of-the-art for the extraction of DDIs is based on feature-engineering algorithms (such as support vector machines), which usually require considerable time and effort. One possible alternative to these approaches includes deep learning. This technique aims to automatically learn the best feature representation from the input data for a given task. The purpose of this paper is to examine whether a convolutional neural network (CNN), which only uses word embeddings as input features, can be applied successfully to classify DDIs from biomedical texts. Proposed herein, is a CNN architecture with only one hidden layer, thus making the model more computationally efficient, and we perform detailed experiments in order to determine the best settings of the model. The goal is to determine the best parameter of this basic CNN that should be considered for future research. The experimental results show that the proposed approach is promising because it attained the second position in the 2013 rankings of the DDI extraction challenge. However, it obtained worse results than previous works using neural networks with more complex architectures. äExploring convolutional neural networks for drug?drug interaction extr(epmc).pdf DeepDyve Pubget Overpricing