scholarly journals Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Narjes Rohani ◽  
Changiz Eslahchi
Keyword(s):  
Neural Network ◽  
Drug Interaction ◽  
Side Effect ◽  
Network Architecture ◽  
Selection Process ◽  
Superior Performance ◽  
Multiple Drug ◽  
Interaction Prediction ◽  
Benchmark Datasets ◽  
Drug Drug Interaction

Abstract Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and health. Proposing appropriate computational methods for predicting unknown DDI with high precision is challenging. We proposed "NDD: Neural network-based method for drug-drug interaction prediction" for predicting unknown DDIs using various information about drugs. Multiple drug similarities based on drug substructure, target, side effect, off-label side effect, pathway, transporter, and indication data are calculated. At first, NDD uses a heuristic similarity selection process and then integrates the selected similarities with a nonlinear similarity fusion method to achieve high-level features. Afterward, it uses a neural network for interaction prediction. The similarity selection and similarity integration parts of NDD have been proposed in previous studies of other problems. Our novelty is to combine these parts with new neural network architecture and apply these approaches in the context of DDI prediction. We compared NDD with six machine learning classifiers and six state-of-the-art graph-based methods on three benchmark datasets. NDD achieved superior performance in cross-validation with AUPR ranging from 0.830 to 0.947, AUC from 0.954 to 0.994 and F-measure from 0.772 to 0.902. Moreover, cumulative evidence in case studies on numerous drug pairs, further confirm the ability of NDD to predict unknown DDIs. The evaluations corroborate that NDD is an efficient method for predicting unknown DDIs. The data and implementation of NDD are available at https://github.com/nrohani/NDD.

scholarly journals Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks

2021 ◽  
Author(s):  
Shichao Liu ◽  
Yang Zhang ◽  
Yuxin Cui ◽  
Yang Qiu ◽  
Yifan Deng ◽  
...  
Keyword(s):  
Neural Network ◽  
Drug Interaction ◽  
Drug Interactions ◽  
Data Sources ◽  
Multiple Drug ◽  
Interaction Prediction ◽  
Multiple Data ◽  
Drug Drug Interaction ◽  
Multiple Drugs ◽  
Novel Drug

AbstractDrug-drug interactions are one of the main concerns in drug discovery. Accurate prediction of drug-drug interactions plays a key role in increasing the efficiency of drug research and safety when multiple drugs are c o-prescribed. With various data sources that describe the relationships and properties between drugs, the comprehensive approach that integrates multiple data sources would be considerably effective in making high-accuracy prediction. In this paper, we propose a Deep Attention Neural Network based Drug-Drug Interaction prediction framework, abbreviated as DANN-DDI, to predict unobserved drug-drug interactions. First, we construct multiple drug feature networks and learn drug representations from these networks using the graph embedding method; then, we concatenate the learned drug embeddings and design an attention neural network to learn representations of drug-drug pairs; finally, we adopt a deep neural network to accurately predict drug-drug interactions. The experimental results demonstrate that our model DANN-DDI has improved prediction performance compared with state-of-the-art methods. Moreover, the proposed model can predict novel drug-drug interactions and drug-drug interaction-associated events.

SmileGNN: Drug-Drug Interaction Prediction Based on SMILES and Graph Neural Network

2021 ◽  
Author(s):  
Xueting Han ◽  
Xutao Li ◽  
Junyi Li
Keyword(s):  
Neural Network ◽  
Drug Interaction ◽  
Prediction Model ◽  
Structural Features ◽  
Interaction Prediction ◽  
Topological Features ◽  
Drug Drug Interaction ◽  
Multiple Drugs ◽  
Interaction Between Drugs

Abstract Background: The use of multiple drugs at the same time can lead to unexpected adverse drug reactions. The interaction between drugs can be confirmed by routine in vitro and clinical trials. But it is difficult to test the drug-drug interaction widely and effectively before the drug is put into market. Therefore, the prediction of drug-drug interaction has become an important research in biomedical field.Results: In recent years, researchers have used deep learning to predict drug-drug interaction by using drug structural features and graph theory, and they have achieved a series of achievements. A drug-drug interaction prediction model SmileGNN is proposed in this paper. The structural features of drugs are constructed by using SMILES data. The topological features of drugs in knowledge graph are obtained by graph neural network. The structural and topological features of drugs are aggregated to predict the interaction of new drug pairs. Conclusions: The experimental results show that the model proposed in this paper combines a variety of data sources, and has better prediction performance compared with the existing prediction model of drug-drug interaction prediction. The most striking result is that five out of top ten predicted new interaction of drugs are verified from the latest database, which proves the credibility of SmileGNN.

scholarly journals An efficient pruning scheme of deep neural networks for Internet of Things applications

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chen Qi ◽  
Shibo Shen ◽  
Rongpeng Li ◽  
Zhifeng Zhao ◽  
Qing Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Internet Of Things ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Superior Performance ◽  
Compact Structure ◽  
Resource Limited ◽  
Benchmark Datasets ◽  
Iot Devices

AbstractNowadays, deep neural networks (DNNs) have been rapidly deployed to realize a number of functionalities like sensing, imaging, classification, recognition, etc. However, the computational-intensive requirement of DNNs makes it difficult to be applicable for resource-limited Internet of Things (IoT) devices. In this paper, we propose a novel pruning-based paradigm that aims to reduce the computational cost of DNNs, by uncovering a more compact structure and learning the effective weights therein, on the basis of not compromising the expressive capability of DNNs. In particular, our algorithm can achieve efficient end-to-end training that transfers a redundant neural network to a compact one with a specifically targeted compression rate directly. We comprehensively evaluate our approach on various representative benchmark datasets and compared with typical advanced convolutional neural network (CNN) architectures. The experimental results verify the superior performance and robust effectiveness of our scheme. For example, when pruning VGG on CIFAR-10, our proposed scheme is able to significantly reduce its FLOPs (floating-point operations) and number of parameters with a proportion of 76.2% and 94.1%, respectively, while still maintaining a satisfactory accuracy. To sum up, our scheme could facilitate the integration of DNNs into the common machine-learning-based IoT framework and establish distributed training of neural networks in both cloud and edge.

scholarly journals FetNet: a recurrent convolutional network for occlusion identification in fetoscopic videos

2020 ◽  
Vol 15 (5) ◽  
pp. 791-801 ◽  
Author(s):  
Sophia Bano ◽  
Francisco Vasconcelos ◽  
Emmanuel Vander Poorten ◽  
Tom Vercauteren ◽  
Sebastien Ourselin ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Laser Photocoagulation ◽  
Short Term Memory ◽  
Total Duration ◽  
Frame Rate ◽  
Superior Performance ◽  
Computer Assisted ◽  
Convolutional Network ◽  
Spatio Temporal

Abstract Purpose Fetoscopic laser photocoagulation is a minimally invasive surgery for the treatment of twin-to-twin transfusion syndrome (TTTS). By using a lens/fibre-optic scope, inserted into the amniotic cavity, the abnormal placental vascular anastomoses are identified and ablated to regulate blood flow to both fetuses. Limited field-of-view, occlusions due to fetus presence and low visibility make it difficult to identify all vascular anastomoses. Automatic computer-assisted techniques may provide better understanding of the anatomical structure during surgery for risk-free laser photocoagulation and may facilitate in improving mosaics from fetoscopic videos. Methods We propose FetNet, a combined convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neural network architecture for the spatio-temporal identification of fetoscopic events. We adapt an existing CNN architecture for spatial feature extraction and integrated it with the LSTM network for end-to-end spatio-temporal inference. We introduce differential learning rates during the model training to effectively utilising the pre-trained CNN weights. This may support computer-assisted interventions (CAI) during fetoscopic laser photocoagulation. Results We perform quantitative evaluation of our method using 7 in vivo fetoscopic videos captured from different human TTTS cases. The total duration of these videos was 5551 s (138,780 frames). To test the robustness of the proposed approach, we perform 7-fold cross-validation where each video is treated as a hold-out or test set and training is performed using the remaining videos. Conclusion FetNet achieved superior performance compared to the existing CNN-based methods and provided improved inference because of the spatio-temporal information modelling. Online testing of FetNet, using a Tesla V100-DGXS-32GB GPU, achieved a frame rate of 114 fps. These results show that our method could potentially provide a real-time solution for CAI and automating occlusion and photocoagulation identification during fetoscopic procedures.

scholarly journals An Explainable Multi-Modal Neural Network Architecture for Predicting Epilepsy Comorbidities Based on Administrative Claims Data

2020 ◽  
Author(s):  
Thomas Linden ◽  
Johann de Jong ◽  
Chao Lu ◽  
Victor Kiri ◽  
Kathrin Haeffs ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Claims Data ◽  
Health Claims ◽  
Superior Performance ◽  
Patient Specific ◽  
Administrative Claims ◽  
Theoretic Approach ◽  
Neural Network Architecture ◽  
Health Claims Data

1AbstractEpilepsy is a complex brain disorder characterized by repetitive seizure events. Epilepsy patients often suffer from various and severe physical and psychological co-morbidities (e.g. anxiety, migraine, stroke, etc.). While general comorbidity prevalences and incidences can be estimated from epidemiological data, such an approach does not take into account that actual patient specific risks can depend on various individual factors, including medication. This motivates to develop a machine learning approach for predicting risks of future comorbidities for the individual epilepsy patient.In this work we use inpatient and outpatient administrative health claims data of around 19,500 US epilepsy patients. We suggest a dedicated multi-modal neural network architecture (Deep personalized LOngitudinal convolutional RIsk model - DeepLORI) to predict the time dependent risk of six common comorbidities of epilepsy patients. We demonstrate superior performance of DeepLORI in a comparison with several existing methods Moreover, we show that DeepLORI based predictions can be interpreted on the level of individual patients. Using a game theoretic approach, we identify relevant features in DeepLORI models and demonstrate that model predictions are explainable in the light of existing knowledge about the disease. Finally, we validate the model on independent data from around 97,000 patients, showing good generalization and stable prediction performance over time.

scholarly journals ON SOME BOUNDS OF THE MINIMUM EDGE DOMINATING ENERGY OF A GRAPH

2021 ◽  
Vol 12 (4) ◽  
pp. 1285-1296
Author(s):  
Dr. Gauri Ghule , Et. al.
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Fuzzy Controller ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Superior Performance ◽  
Fuzzy Classifier ◽  
Manual Intervention ◽  
The Neural Network ◽  
Hidden Neurons

Number of hidden neurons is necessary constant for tuning the neural network to achieve superior performance. These parameters are set manually through experimentation. The performance of the network is evaluated repeatedly to choose the best input parameters.Random selection of hidden neurons may cause underfitting or overfitting of the network. We propose a novel fuzzy controller for finding the optimal value of hidden neurons automatically. The hybrid classifier helps to design competent neural network architecture, eliminating manual intervention for setting the input parameters. The effectiveness of tuning the number of hidden neurons automatically on the convergence of a back-propagation neural network, is verified on speech data. The experimental outcomes demonstrate that the proposed Neuro-Fuzzy classifier can be viably utilized for speech recognition with maximum classification accuracy.

Drug-Drug Interaction Prediction: a Purely SMILES Based Approach

2021 ◽  
Author(s):  
Bri Bumgardner ◽  
Farhan Tanvir ◽  
Khaled Mohammed Saifuddin ◽  
Esra Akbas
Keyword(s):  
Drug Interaction ◽  
Interaction Prediction ◽  
Drug Drug Interaction

MDIPA: a microRNA–drug interaction prediction approach based on non-negative matrix factorization

2020 ◽  
Vol 36 (20) ◽  
pp. 5061-5067
Author(s):  
Ali Akbar Jamali ◽  
Anthony Kusalik ◽  
Fang-Xiang Wu
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Matrix Factorization ◽  
Structural Information ◽  
Superior Performance ◽  
Supplementary Information ◽  
Similarity Matrix ◽  
Interaction Prediction ◽  
Prediction Approach ◽  
Drug Similarity

Abstract Motivation Evidence has shown that microRNAs, one type of small biomolecule, regulate the expression level of genes and play an important role in the development or treatment of diseases. Drugs, as important chemical compounds, can interact with microRNAs and change their functions. The experimental identification of microRNA–drug interactions is time-consuming and expensive. Therefore, it is appealing to develop effective computational approaches for predicting microRNA–drug interactions. Results In this study, a matrix factorization-based method, called the microRNA–drug interaction prediction approach (MDIPA), is proposed for predicting unknown interactions among microRNAs and drugs. Specifically, MDIPA utilizes experimentally validated interactions between drugs and microRNAs, drug similarity and microRNA similarity to predict undiscovered interactions. A path-based microRNA similarity matrix is constructed, while the structural information of drugs is used to establish a drug similarity matrix. To evaluate its performance, our MDIPA is compared with four state-of-the-art prediction methods with an independent dataset and cross-validation. The results of both evaluation methods confirm the superior performance of MDIPA over other methods. Finally, the results of molecular docking in a case study with breast cancer confirm the efficacy of our approach. In conclusion, MDIPA can be effective in predicting potential microRNA–drug interactions. Availability and implementation All code and data are freely available from https://github.com/AliJam82/MDIPA. Supplementary information Supplementary data are available at Bioinformatics online.

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