Cargando…

Drug drug interaction extraction from the literature using a recursive neural network

Detecting drug-drug interactions (DDI) is important because information on DDIs can help prevent adverse effects from drug combinations. Since there are many new DDI-related papers published in the biomedical domain, manually extracting DDI information from the literature is a laborious task. Howeve...

Descripción completa

Detalles Bibliográficos
Autores principales:	Lim, Sangrak, Lee, Kyubum, Kang, Jaewoo
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Public Library of Science 2018
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5786304/ https://www.ncbi.nlm.nih.gov/pubmed/29373599 http://dx.doi.org/10.1371/journal.pone.0190926

Ejemplares similares

Chemical–gene relation extraction using recursive neural network
por: Lim, Sangrak, et al.
Publicado: (2018)

Drug drug interaction extraction from biomedical literature using syntax convolutional neural network
por: Zhao, Zhehuan, et al.
Publicado: (2016)

BEST: Next-Generation Biomedical Entity Search Tool for Knowledge Discovery from Biomedical Literature
por: Lee, Sunwon, et al.
Publicado: (2016)

Deep learning of mutation-gene-drug relations from the literature
por: Lee, Kyubum, et al.
Publicado: (2018)

BRONCO: Biomedical entity Relation ONcology COrpus for extracting gene-variant-disease-drug relations
por: Lee, Kyubum, et al.
Publicado: (2016)

Recursive neural networks for associative memory
por: Kamp, Yves, et al.
Publicado: (1990)

Drug-Drug Interaction Extraction via Convolutional Neural Networks
por: Liu, Shengyu, et al.
Publicado: (2016)

Exploring convolutional neural networks for drug–drug interaction extraction
por: Suárez-Paniagua, Víctor, et al.
Publicado: (2017)

Recursive Neural Networks in Quark/Gluon Tagging
por: Cheng, Taoli
Publicado: (2018)

Prediction of drug-drug interaction events using graph neural networks based feature extraction
por: Al-Rabeah, Mohammad Hussain, et al.
Publicado: (2022)

Recursive neural networks in hospital bed occupancy forecasting
por: Kutafina, Ekaterina, et al.
Publicado: (2019)

Using drug descriptions and molecular structures for drug–drug interaction extraction from literature
por: Asada, Masaki, et al.
Publicado: (2020)

On the efficacy of per-relation basis performance evaluation for PPI extraction and a high-precision rule-based approach
por: Lee, Junkyu, et al.
Publicado: (2013)

Neural correlates of recursive thinking during interpersonal strategic interactions
por: Zhen, Shanshan, et al.
Publicado: (2021)

Extraction of Pharmacokinetic Evidence of Drug–Drug Interactions from the Literature
por: Kolchinsky, Artemy, et al.
Publicado: (2015)

Deeply-recursive convolutional neural network for Raman spectra identification
por: Zhou, Wei, et al.
Publicado: (2022)

Self-Supervised Denoising Image Filter Based on Recursive Deep Neural Network Structure
por: Kang, Changhee, et al.
Publicado: (2021)

Extracting drug-enzyme relation from literature as evidence for drug drug interaction
por: Zhang, Yaoyun, et al.
Publicado: (2016)

Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity
por: Rohani, Narjes, et al.
Publicado: (2019)

Erratum to: Extracting drug-enzyme relation from literature as evidence for drug drug interaction
por: Zhang, Yaoyun, et al.
Publicado: (2016)

Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition
por: Li, Qi, et al.
Publicado: (2022)

Drug-Drug Interaction Extraction via Recurrent Hybrid Convolutional Neural Networks with an Improved Focal Loss
por: Sun, Xia, et al.
Publicado: (2019)

A recursive framework for predicting the time-course of drug sensitivity
por: Qian, Cheng, et al.
Publicado: (2020)

An attention-based effective neural model for drug-drug interactions extraction
por: Zheng, Wei, et al.
Publicado: (2017)

Integrating heterogeneous knowledge graphs into drug–drug interaction extraction from the literature
por: Asada, Masaki, et al.
Publicado: (2022)

Recursive module extraction using Louvain and PageRank
por: Perrin, Dimitri, et al.
Publicado: (2018)

Ab initio and homology based prediction of protein domains by recursive neural networks
por: Walsh, Ian, et al.
Publicado: (2009)

NNcon: improved protein contact map prediction using 2D-recursive neural networks
por: Tegge, Allison N., et al.
Publicado: (2009)

Extracting Drug-Drug Interaction from the Biomedical Literature Using a Stacked Generalization-Based Approach
por: He, Linna, et al.
Publicado: (2013)

Enhancing Extraction of Drug-Drug Interaction from Literature Using Neutral Candidates, Negation, and Clause Dependency
por: Bokharaeian, Behrouz, et al.
Publicado: (2016)

Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs
por: Feng, Yue-Hua, et al.
Publicado: (2022)

Recursive Random Lasso (RRLasso) for Identifying Anti-Cancer Drug Targets
por: Park, Heewon, et al.
Publicado: (2015)

A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis
por: Kim, Seongsoon, et al.
Publicado: (2018)

Recent advances of automated methods for searching and extracting genomic variant information from biomedical literature
por: Lee, Kyubum, et al.
Publicado: (2020)

Recursiveness
por: Eilenberg, Samuel, et al.
Publicado: (1970)

On recursion
por: Watumull, Jeffrey, et al.
Publicado: (2014)

Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks
por: Kukic, Predrag, et al.
Publicado: (2014)

A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network
por: Javeed, Madiha, et al.
Publicado: (2023)

Logistics Distribution Route Optimization Model Based on Recursive Fuzzy Neural Network Algorithm
por: Liu, Binbin
Publicado: (2021)

Deep Learning of Subject Context in Ideological and Political Class Based on Recursive Neural Network
por: Jiang, Tingting, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_mjo84uadlv5nqod8l2lo7f9k75