Journal Article10.1016/j.eswa.2022.119312
DAEM: Deep attributed embedding based multi-task learning for predicting adverse drug-drug interaction
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TL;DR: Zhang et al. as mentioned in this paper proposed a Deep Attributed Embedding based Multi-task (DAEM) learning model for ADDI prediction, which embeds the hand-designed attributes into their low-dimensional spaces while preserving adverse relationship and modeling attribute dependence for learning the informative attribute representations and capturing the nonlinear properties of drugs.
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Abstract: Adverse drug–drug interaction (ADDI) is an important concern in pharmaceutical industry and becomes a leading cause of morbidity and mortality in public health. With the increasing accumulation of biochemical characteristics of drugs, many computational methods are proposed by exploiting multiple attributes of drugs for ADDI prediction. However, due to the high-dimensional and highly sparse spaces of the hand-designed attributes of drugs, it still remains a challenging issue for investigating a robust projection between attributes of drugs and their adverse interactions, which can benefit to revealing the non-linear properties of their adverse relationship for accurate ADDI prediction. In this paper, we propose a Deep Attributed Embedding based Multi-task (DAEM) learning model for ADDI prediction. In particular, two drug attributes, molecular structure and side effect, are adopted to model the adverse interactions among drugs and a deep neural network is designed to embed the hand-designed attributes into their low-dimensional spaces while preserving adverse relationship and modeling attribute dependence for learning the informative attribute representations and capturing the non-linear properties of drugs. Along this line, multi-task learning is performed for ADDI prediction by regarding the prediction of each ADDI as a regression task jointly with proper regularizations. Experimental results on real-world dataset demonstrate the effectiveness of DAEM when compared with thirteen baselines and its variants.
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Citations
MTMol-GPT: De novo multi-target molecular generation with transformer-based generative adversarial imitation learning
Chengwei Ai,Hongpeng Yang,Xiaoyi Liu,Ruijuan Dong,Fei Guo,Fei Guo +5 more
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EMSI-BERT: Asymmetrical Entity-Mask Strategy and Symbol-Insert Structure for Drug-Drug Interaction Extraction Based on BERT
TL;DR: In this paper , the authors proposed a novel EMSI-BERT method for drug-drug interaction extraction based on an asymmetrical entity-mask strategy and a Symbol-insert structure to address the weak representation of co-occurring entity information using the drug entity dictionary in the pre-training BERT task.
Robust Adverse Drug Reaction Prediction and Classification by Employing Deer Hunting Optimization Driven Deep Learning Approach
TL;DR: In this paper , the authors introduced a Deer Hunting Optimization Driven Deep Learning Model for Robust Adverse Drug Reaction Recognition and Classification (DHODL-ADRRC) technique which involves diverse phases of data preprocessing to normalize the data.
pADR: Towards Personalized Adverse Drug Reaction Prediction by Modeling Multi-sourced Data
Junyu Luo,Cheng Qian,Xiaochen Wang,Lucas Glass,Fenglong Ma +4 more
- 21 Oct 2023
TL;DR: Experimental results on a new multi-sourced ADR prediction dataset show that pADR1 outperforms state-of-the-art drug-based baselines and the effectiveness of the proposed fusion strategies and the reasonableness of each module design.
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References
The SIDER database of drugs and side effects
TL;DR: The SIDER (‘Side Effect Resource’, http://sideeffects.embl.de) database of drugs and ADRs contains a data set of drug indications, extracted from the package inserts using Natural Language Processing, used to reduce the rate of false positives by identifying medical terms that do not correspond to ADRs.
1.3K
Modeling polypharmacy side effects with graph convolutional networks.
TL;DR: Decagon is presented, an approach for modeling polypharmacy side effects that develops a new graph convolutional neural network for multirelational link prediction in multimodal networks and can predict the exact side effect, if any, through which a given drug combination manifests clinically.
1.2K
Data-Driven Prediction of Drug Effects and Interactions
TL;DR: Better than tarot cards or crystal balls, the authors show that intricate analyses of observational clinical data can improve physicians’ ability to predict the future—at least with respect to as yet uncharacterized adverse drug effects and interactions.
858
Label Informed Attributed Network Embedding
Xiao Huang,Jundong Li,Xia Hu +2 more
- 02 Feb 2017
TL;DR: A novel Label informed Attributed Network Embedding (LANE) framework that can smoothly incorporate label information into the attributed network embedding while preserving their correlations is proposed and achieves significantly better performance compared with the state-of-the-art embedding algorithms.
607
Attributed Social Network Embedding
TL;DR: This paper proposes a generic Attributed Social Network Embedding framework (ASNE), which learns representations for social actors by preserving both the structural proximity and attribute proximity, and shows significant gains on the tasks of link prediction and node classification.