scholarly journals Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kanglin Hsieh ◽  
Yinyin Wang ◽  
Luyao Chen ◽  
Zhongming Zhao ◽  
Sean Savitz ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Drug Repurposing ◽  
Population Based ◽  
Rapid Identification ◽  
Patient Treatment ◽  
Biological Interactions ◽  
Neural Network Approach ◽  
Trial History

AbstractSince the 2019 novel coronavirus disease (COVID-19) outbreak in 2019 and the pandemic continues for more than one year, a vast amount of drug research has been conducted and few of them got FDA approval. Our objective is to prioritize repurposable drugs using a pipeline that systematically integrates the interaction between COVID-19 and drugs, deep graph neural networks, and in vitro/population-based validations. We first collected all available drugs (n = 3635) related to COVID-19 patient treatment through CTDbase. We built a COVID-19 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate drug’s representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and population-based treatment effect. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and multiple evidence can facilitate the rapid identification of candidate drugs for COVID-19 treatment.

scholarly journals Drug Repurposing for COVID-19 using Graph Neural Network with Genetic, Mechanistic, and Epidemiological Validation

2020 ◽  
Author(s):  
Kang-Lin Hsieh ◽  
Yinyin Wang ◽  
Luyao Chen ◽  
Zhongming Zhao ◽  
Sean Savitz ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Drug Repurposing ◽  
Population Based ◽  
Rapid Identification ◽  
Patient Treatment ◽  
Biological Interactions ◽  
Neural Network Approach ◽  
Trial History

Abstract Amid the pandemic of 2019 novel coronavirus disease (COVID-19) infected by SARS-CoV-2, a vast amount of drug research for prevention and treatment has been quickly conducted, but these efforts have been unsuccessful thus far. Our objective is to prioritize repurposable drugs using a drug repurposing pipeline that systematically integrates multiple SARS-CoV-2 and drug interactions, deep graph neural networks, and in-vitro/population-based validations. We first collected all the available drugs (n= 3,635) involved in COVID-19 patient treatment through CTDbase. We built a SARS-CoV-2 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate drug’s representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and electronic health records. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment. This paper had been uploaded to arXiv : https://arxiv.org/abs/2009.10931

Estimation of Annual Average Daily Traffic on Low-Volume Roads: Factor Approach Versus Neural Networks

2000 ◽  
Vol 1719 (1) ◽  
pp. 103-111 ◽  
Author(s):  
Satish C. Sharma ◽  
Pawan Lingras ◽  
Guo X. Liu ◽  
Fei Xu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Traffic Monitoring ◽  
Network Approach ◽  
Annual Average ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Low Volume Roads ◽  
Annual Average Daily Traffic ◽  
Low Volume

Estimation of the annual average daily traffic (AADT) for low-volume roads is investigated. Artificial neural networks are compared with the traditional factor approach for estimating AADT from short-period traffic counts. Fifty-five automatic traffic recorder (ATR) sites located on low-volume rural roads in Alberta, Canada, are used as study samples. The results of this study indicate that, when a single 48-h count is used for AADT estimation, the factor approach can yield better results than the neural networks if the ATR sites are grouped appropriately and the sample sites are correctly assigned to various ATR groups. Unfortunately, the current recommended practice offers little guidance on how to achieve the assignment accuracy that may be necessary to obtain reliable AADT estimates from a single 48-h count. The neural network approach can be particularly suitable for estimating AADT from two 48-h counts taken at different times during the counting season. In fact, the 95th percentile error values of about 25 percent as obtained in this study for the neural network models compare favorably with the values reported in the literature for low-volume roads using the traditional factor approach. The advantage of the neural network approach is that classification of ATR sites and sample site assignments to ATR groups are not required. The analysis of various groups of low-volume roads presented also leads to a conclusion that, when defining low-volume roads from a traffic monitoring point of view, it is not likely to matter much whether the AADT on the facility is less than 500 vehicles, less than 750 vehicles, or less than 1,000 vehicles.

scholarly journals Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 588
Author(s):  
Felipe Leite Coelho da Silva ◽  
Kleyton da Costa ◽  
Paulo Canas Rodrigues ◽  
Rodrigo Salas ◽  
Javier Linkolk López-Gonzales
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Approach ◽  
Electricity Consumption ◽  
Industrial Sector ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
The One ◽  
Mlp Model

Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multilayer perceptron) models were considered. The results indicate that the MLP model was the one that obtained the best forecasting performance for the electricity consumption of the Brazilian industry under analysis.

scholarly journals Artificial Neural Network in Drug Delivery and Pharmaceutical Research

2013 ◽  
Vol 7 (1) ◽  
pp. 49-62 ◽  
Author(s):  
Vijaykumar Sutariya ◽  
Anastasia Groshev ◽  
Prabodh Sadana ◽  
Deepak Bhatia ◽  
Yashwant Pathak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Drug Delivery ◽  
Pattern Recognition ◽  
Analytical Data ◽  
Pharmaceutical Research ◽  
Artificial Neural ◽  
The Brain

Artificial neural networks (ANNs) technology models the pattern recognition capabilities of the neural networks of the brain. Similarly to a single neuron in the brain, artificial neuron unit receives inputs from many external sources, processes them, and makes decisions. Interestingly, ANN simulates the biological nervous system and draws on analogues of adaptive biological neurons. ANNs do not require rigidly structured experimental designs and can map functions using historical or incomplete data, which makes them a powerful tool for simulation of various non-linear systems.ANNs have many applications in various fields, including engineering, psychology, medicinal chemistry and pharmaceutical research. Because of their capacity for making predictions, pattern recognition, and modeling, ANNs have been very useful in many aspects of pharmaceutical research including modeling of the brain neural network, analytical data analysis, drug modeling, protein structure and function, dosage optimization and manufacturing, pharmacokinetics and pharmacodynamics modeling, and in vitro in vivo correlations. This review discusses the applications of ANNs in drug delivery and pharmacological research.

Individualizing Displays through the Use of Neural Networks

1997 ◽  
Vol 41 (1) ◽  
pp. 269-273
Author(s):  
Kai-Chun Cheng ◽  
Ray E. Eberts
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Intelligent Vehicle ◽  
Network Approach ◽  
Neural Network Approach ◽  
Traveler Information ◽  
Gas Stations ◽  
The Neural Network ◽  
Advanced Traveler Information System ◽  
Near Future

An Advanced Traveler Information System (ATIS), a key component of Intelligent Vehicle highway Systems (IVHS) in the near future, will help travelers find locations of restaurants, lodging, gas stations, and rest stops. On typical ATIS displays, which are now being incorporated in some advanced vehicles, the choices for these traveler services are presented to the vehicle occupants alphabetically. An experiment was conducted to determine whether individualizing the display through the use of neural networks enhanced performance when choosing restaurants. The neural network ATIS was compared to an ATIS that displayed the most frequently chosen restaurants at the top, one that alphabetized the list of restaurants, and one that randomly displayed the restaurant choices. The time to choose a restaurant was significantly faster for the individualized displays (neural network and frequency) when compared to the nonindividualized displays (alphabetical and random). When the two individualized displays were compared, choice time was significantly faster for the neural network approach.

The Application of RBF Neural Networks in Curve Fitting

2012 ◽  
Vol 490-495 ◽  
pp. 688-692
Author(s):  
Zhong Biao Sheng ◽  
Xiao Rong Tong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Rbf Neural Network ◽  
Engineering Application ◽  
Nonlinear Function ◽  
Rbf Neural Networks ◽  
Neural Network Approach ◽  
Rbf Network ◽  
Fast Learning ◽  
Nonlinear Processing

Three means to realize function approach such as the interpolation approach, fitting approach as well as the neural network approach are discussed based on Matlab to meet the demand of data processing in engineering application. Based on basic principle of introduction, realization methods to non-linear are researched using interpolation function and fitting function in Matlab with example. It mainly studies the RBF neural networks and the training method. RBF neural network to proximate nonlinear function is designed and the desired effect is achieved through the training and simulation of network. As is shown from the simulation results, RBF network has strong nonlinear processing and approximating features, and RBF network model has the characteristics of high precision, fast learning speed for the prediction.

scholarly journals Neural Networks for Postprocessing Ensemble Weather Forecasts

2018 ◽  
Vol 146 (11) ◽  
pp. 3885-3900 ◽  
Author(s):  
Stephan Rasp ◽  
Sebastian Lerch
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Predictive Distribution ◽  
Predictor Variables ◽  
Specific Information ◽  
Observation Data ◽  
Neural Network Approach ◽  
Weather Forecasts ◽  
Probabilistic Forecasts ◽  
Trained Neural Network

Abstract Ensemble weather predictions require statistical postprocessing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the parameters of a predictive distribution are estimated from a training period. We propose a flexible alternative based on neural networks that can incorporate nonlinear relationships between arbitrary predictor variables and forecast distribution parameters that are automatically learned in a data-driven way rather than requiring prespecified link functions. In a case study of 2-m temperature forecasts at surface stations in Germany, the neural network approach significantly outperforms benchmark postprocessing methods while being computationally more affordable. Key components to this improvement are the use of auxiliary predictor variables and station-specific information with the help of embeddings. Furthermore, the trained neural network can be used to gain insight into the importance of meteorological variables, thereby challenging the notion of neural networks as uninterpretable black boxes. Our approach can easily be extended to other statistical postprocessing and forecasting problems. We anticipate that recent advances in deep learning combined with the ever-increasing amounts of model and observation data will transform the postprocessing of numerical weather forecasts in the coming decade.

scholarly journals A Comparison Study of Constitutive Equation, Neural Networks, and Support Vector Regression for Modeling Hot Deformation of 316L Stainless Steel

Materials ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 3766 ◽  
Author(s):  
Shin-Hyung Song
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Stainless Steel ◽  
Flow Stress ◽  
Support Vector Regression ◽  
Hot Deformation ◽  
316L Stainless Steel ◽  
Support Vector ◽  
Network Approach ◽  
Neural Network Approach

In this research, hot deformation experiments of 316L stainless steel were carried out at a temperature range of 800–1000 °C and strain rate of 2 × 10−3–2 × 10−1. The flow stress behavior of 316L stainless steel was found to be highly dependent on the strain rate and temperature. After the experimental study, the flow stress was modeled using the Arrhenius-type constitutive equation, a neural network approach, and the support vector regression algorithm. The present research mainly focused on a comparative study of three algorithms for modeling the characteristics of hot deformation. The results indicated that the neural network approach and the support vector regression algorithm could be used to model the flow stress better than the approach of the Arrhenius-type equation. The modeling efficiency of the support vector regression algorithm was also found to be more efficient than the algorithm for neural networks.

Prediction of dissolved oxygen in the Mediterranean Sea along Gaza, Palestine – an artificial neural network approach

2009 ◽  
Vol 60 (12) ◽  
pp. 3051-3059 ◽  
Author(s):  
Hossam Adel Zaqoot ◽  
Abdul Khalique Ansari ◽  
Mukhtiar Ali Unar ◽  
Shaukat Hyat Khan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Dissolved Oxygen ◽  
Radial Basis Function ◽  
Mediterranean Sea ◽  
Basis Function ◽  
Data Sets ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Mediterranean

Artificial Neural Networks (ANNs) are flexible tools which are being used increasingly to predict and forecast water resources variables. The human activities in areas surrounding enclosed and semi-enclosed seas such as the Mediterranean Sea always produce in the long term a strong environmental impact in the form of coastal and marine degradation. The presence of dissolved oxygen is essential for the survival of most organisms in the water bodies. This paper is concerned with the use of ANNs — Multilayer Perceptron (MLP) and Radial Basis Function neural networks for predicting the next fortnight’s dissolved oxygen concentrations in the Mediterranean Sea water along Gaza. MLP and Radial Basis Function (RBF) neural networks are trained and developed with reference to five important oceanographic variables including water temperature, wind velocity, turbidity, pH and conductivity. These variables are considered as inputs of the network. The data sets used in this study consist of four years and collected from nine locations along Gaza coast. The network performance has been tested with different data sets and the results show satisfactory performance. Prediction results prove that neural network approach has good adaptability and extensive applicability for modelling the dissolved oxygen in the Mediterranean Sea along Gaza. We hope that the established model will help in assisting the local authorities in developing plans and policies to reduce the pollution along Gaza coastal waters to acceptable levels.

scholarly journals A Neural Network Approach to Pricing in the Precious Metals Market

2019 ◽  
Author(s):  
V. E. Bilozorov ◽  
A. S. Ivlev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Experimental Studies ◽  
Precious Metals ◽  
Mathematical Statistics ◽  
Coefficient Of Determination ◽  
Future Price ◽  
Neural Network Approach ◽  
Modern Development ◽  
Artificial Neural

The modern development of the science of artificial neural networks (ANN) has allowed to use their nature and properties in various applied fields of science. One of the most important applications of ANN is the modeling of prices in the precious metals market. Just like in any other market, based on the prediction of current prices, because the ability of ANN to learn like a true biological neural network, relying on the input with subsequent testing of the output, provides a significant advantage in the prediction tasks compared to the classical mathematical algorithms. Predicting the price of precious metals with relatively high precision and low error is in great demand among all individuals and legal entities that carry out transactions which are directly related to the purchase and sale of these precious metals, since accurate knowledge of the future price of a particular metal will bring maximum benefits of these operations. Numerous methods have been developed [2-4] for the use of neural networks in the modeling of price forecasts, which make the prediction of the rate of exchange for a particular currency (rather objective). The applied methods make the prediction using the classical perceptron along with astrological cyclic indices [2], recursive neural networks [3], and/or using elements of mathematical statistics (for example, use of U-statistic and the coefficient of determination ) [4]. The goal of this paper is the attempt to usethe ANN in the forecasting problem that allows predicting the price of precious metals in the near future, based on an algorithm that makes predictions by learning based on an array of input data and does not depend on the said elements of mathematical statistics. The paper presents a new method for using an artificial neural network in forecasting problems. Experimental studies of this method were carried out on the basis of the precious metals pricing rate on the Ukrainian Interbank Exchange. The corresponding conclusions are made regarding the effectiveness of the method and the possibilities for its further improvement based on the results of these studies. It is expected that such an algorithm will give a prediction as close as possible to the real value.

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