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Attention-Gated Graph Convolutions for Extracting Drug Interaction Information from Drug Labels [article]

Tung Tran, Ramakanth Kavuluru, Halil Kilicoglu
2019 arXiv   pre-print
In this study, we tackle the problem of jointly extracting drugs and their interactions, including interaction outcome, from drug labels.  ...  As drug-drug interactions (DDIs) may lead to preventable adverse events, being able to extract DDIs from drug labels into a machine-processable form is an important step toward effective dissemination  ...  ACKNOWLEDGEMENTS AND FUNDING This research was partially conducted during TT's participation in the Lister Hill National Center for Biomedical Communications (LHNCBC) Research Program in Medical Informatics  ... 
arXiv:1910.12419v2 fatcat:nmb5j35aczcldg2h4ywoijiojy

Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information [article]

Masaki Asada, Makoto Miwa, Yutaka Sasaki
2018 arXiv   pre-print
We encode textual drug pairs with convolutional neural networks and their molecular pairs with graph convolutional networks (GCNs), and then we concatenate the outputs of these two networks.  ...  We propose a novel neural method to extract drug-drug interactions (DDIs) from texts using external drug molecular structure information.  ...  Data and Task for Molecular Structures We extracted 255,229 interacting (positive) pairs from DrugBank.  ... 
arXiv:1805.05593v1 fatcat:2zhjdpd4yrdzfhxiwwcnscl754

Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information

Masaki Asada, Makoto Miwa, Yutaka Sasaki
2018 Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)  
We encode textual drug pairs with convolutional neural networks and their molecular pairs with graph convolutional networks (GCNs), and then we concatenate the outputs of these two networks.  ...  We propose a novel neural method to extract drug-drug interactions (DDIs) from texts using external drug molecular structure information.  ...  Data and Task for Molecular Structures We extracted 255,229 interacting (positive) pairs from DrugBank.  ... 
doi:10.18653/v1/p18-2108 dblp:conf/acl/AsadaMS18 fatcat:jkahmbxlw5h3naycu3fyhvniba

Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network [article]

Seongok Ryu, Jaechang Lim, Seung Hwan Hong, Woo Youn Kim
2018 arXiv   pre-print
Here we show that the performance of graph convolutional networks (GCNs) for the prediction of molecular properties can be improved by incorporating attention and gate mechanisms.  ...  Molecular structure-property relationships are key to molecular engineering for materials and drug discovery.  ...  Acknowledgements Author contributions S.R. and W.Y.K. conceived the idea, S.R. did the implementation and run the simulation. All the authors analyzed the results and wrote the manuscript together.  ... 
arXiv:1805.10988v3 fatcat:pg52xhs3sbftxa7dnfbowipjay

Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks [article]

Jaechang Lim, Seongok Ryu, Kyubyong Park, Yo Joong Choe, Jiyeon Ham,, Woo Youn Kim
2019 arXiv   pre-print
Accurate prediction of drug-target interaction (DTI) is essential for in silico drug design.  ...  We also apply a distance-aware graph attention algorithm with gate augmentation to increase the performance of our model.  ...  Introduction Accurate prediction of drug-target interaction (DTI) is essential for in silico drug discovery.  ... 
arXiv:1904.08144v1 fatcat:vr2mldf3qrewpd277ampa326aa

Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction [article]

Yingheng Wang, Yaosen Min, Xin Chen, Ji Wu
2020 arXiv   pre-print
This study presents a new method, multi-view graph contrastive representation learning for drug-drug interaction prediction, MIRACLE for brevity, to capture inter-view molecule structure and intra-view  ...  We use GCNs and bond-aware attentive message passing networks to encode DDI relationships and drug molecular graphs in the MIRACLE learning stage, respectively.  ...  We used GIN to encode drug molecular graphs and learn their representations to make predictions as a baseline. • Attentive Graph Autoencoder: Ma et al.  ... 
arXiv:2010.11711v2 fatcat:ryzrp7rosnblhc3bpcgnrmvxp4

A Review on Compound-Protein Interaction Prediction Methods: Data, Format, Representation and Model

Sangsoo Lim, Yijingxiu Lu, Chang Yun Cho, Inyoung Sung, Jungwoo Kim, Youngkuk Kim, Sungjoon Park, Sun Kim
2021 Computational and Structural Biotechnology Journal  
There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI).  ...  Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes.  ...  [53] constructed a dataset for target-driven drug repurposing on GPCR proteins. Drug-drug interaction, drug indications, and FDA-approved from DrugBank were also used by Zeng et al.  ... 
doi:10.1016/j.csbj.2021.03.004 pmid:33841755 pmcid:PMC8008185 fatcat:x3xk3b566vh6ljpeffojtusda4

Relation Extraction from Biomedical and Clinical Text: Unified Multitask Learning Framework [article]

Shweta Yadav, Srivatsa Ramesh, Sriparna Saha, Asif Ekbal
2020 arXiv   pre-print
In this paper, we study the relation extraction task from three major biomedical and clinical tasks, namely drug-drug interaction, protein-protein interaction, and medical concept relation extraction.  ...  learning approach for the prediction of relationships from the biomedical and clinical text.  ...  [45] proposed a tree RNN with structured attention framework for extracting the PPI information. • Drug Drug Interaction task: Existing techniques on drug drug interaction can be categorized into one-stage  ... 
arXiv:2009.09509v1 fatcat:fd3zpfjrybhrdpqkwgvpzrmisi

CID-GCN: An Effective Graph Convolutional Networks for Chemical-Induced Disease Relation Extraction

Daojian Zeng, Chao Zhao, Zhe Quan
2021 Frontiers in Genetics  
Automatic extraction of chemical-induced disease (CID) relation from unstructured text is of essential importance for disease treatment and drug development.  ...  In this paper, we propose CID-GCN, an effective Graph Convolutional Networks (GCNs) with gating mechanism, for CID relation extraction.  ...  Different from previous biomedical relation extraction tasks such as protein-protein interaction (PPI) detection and drug-drug interaction(DDI) detection, the CID relations are determined at document level  ... 
doi:10.3389/fgene.2021.624307 pmid:33643385 pmcid:PMC7902761 fatcat:cwipokeplzbezg6slbj2wi267u

Incorporating representation learning and multihead attention to improve biomedical cross-sentence n-ary relation extraction

Di Zhao, Jian Wang, Yijia Zhang, Xin Wang, Hongfei Lin, Zhihao Yang
2020 BMC Bioinformatics  
In this paper, we propose a novel cross-sentence n-ary relation extraction method that utilizes the multihead attention and knowledge representation that is learned from the knowledge graph.  ...  We explored a novel method for cross-sentence n-ary relation extraction.  ...  Acknowledgments The authors would like to thank the editor and all anonymous reviewers for valuable suggestions and constructive comments.  ... 
doi:10.1186/s12859-020-03629-9 pmid:32677883 fatcat:embkd22td5a6lnarajesvyj4wy

DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction [article]

Xuan Lin
2020 arXiv   pre-print
as the molecular structure from the drugs.  ...  Motivated by this, we propose a novel end-to-end learning framework, called DeepGS, which uses deep neural networks to extract the local chemical context from amino acids and SMILES sequences, as well  ...  Acknowledgements We thank the anonymous reviewers very much for their effort in evaluating our paper. This work was supported in part by the National Key  ... 
arXiv:2003.13902v2 fatcat:ubaimdjexbchhlfgnqvbpgoi7q

Cross-Sentence N-ary Relation Extraction with Graph LSTMs [article]

Nanyun Peng, Hoifung Poon, Chris Quirk, Kristina Toutanova, Wen-tau Yih
2017 arXiv   pre-print
In this paper, we explore a general relation extraction framework based on graph long short-term memory networks (graph LSTMs) that can be easily extended to cross-sentence n-ary relation extraction.  ...  The graph formulation provides a unified way of exploring different LSTM approaches and incorporating various intra-sentential and inter-sentential dependencies, such as sequential, syntactic, and discourse  ...  Acknowledgements We thank Daniel Fried and Ming-Wei Chang for useful discussions, as well as the anonymous reviewers and editor-in-chief Mark Johnson for their helpful comments.  ... 
arXiv:1708.03743v1 fatcat:2cmd6mtg5neq3gexu5fet5pkya

Cross-Sentence N-ary Relation Extraction with Graph LSTMs

Nanyun Peng, Hoifung Poon, Chris Quirk, Kristina Toutanova, Wen-tau Yih
2017 Transactions of the Association for Computational Linguistics  
In this paper, we explore a general relation extraction framework based on graph long short-term memory networks (graph LSTMs) that can be easily extended to cross-sentence n-ary relation extraction.  ...  The graph formulation provides a unified way of exploring different LSTM approaches and incorporating various intra-sentential and intersentential dependencies, such as sequential, syntactic, and discourse  ...  Acknowledgements We thank Daniel Fried and Ming-Wei Chang for useful discussions, as well as the anonymous reviewers and editor-in-chief Mark Johnson for their helpful comments.  ... 
doi:10.1162/tacl_a_00049 fatcat:ey7qk2gwqrebjkzpifjmqjvfq4

Drug-Drug Interaction Extraction Based on Bidirectional Gated Recurrent Unit networks and Capsule Networks

Wenzhun Huang
2021 Open Access Journal of Biomedical Science  
The method integrates a reference standard database of known DDIs with drug similarity information extracted from different sources, such as 2D and 3D molecular structures, interaction profiles, targets  ...  Different from LSTM, it combines the forget gate and the input gate as a single update gate and adds the cell state and hidden state with other changes.  ... 
doi:10.38125/oajbs.000247 fatcat:pzhnadtp5rfd5aa2qe4zwqvbbq

Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction [article]

QHwan Kim, Joon-Hyuk Ko, Sunghoon Kim, Nojun Park, Wonho Jhe
2020 arXiv   pre-print
The deep learning-based approaches have attracted attention because they can predict drug-protein interactions without trial-and-error by humans.  ...  The characterization of drug-protein interactions is crucial in the high-throughput screening for drug discovery.  ...  Acknowledgements Author contributions statement All authors contributed to construct concept and initialize the project. Q.K and W.J made the program.  ... 
arXiv:2012.08194v2 fatcat:52iiq2wacjgstbjz7bwpdp5be4
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