scholarly journals Potential neutralizing antibodies discovered for novel corona virus using machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rishikesh Magar ◽  
Prakarsh Yadav ◽  
Amir Barati Farimani
Keyword(s):  
Machine Learning ◽  
Neutralizing Antibodies ◽  
Md Simulations ◽  
Support Vector ◽  
Inhibitory Antibody ◽  
Clinical Patient ◽  
Multilayered Perceptron ◽  
The Stability ◽  
High Throughput Manner ◽  
Neutralization Response

AbstractThe fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. The recent outbreak of COVID-19 infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of SARS-CoV-2 will save the life of thousands. To predict neutralizing antibodies for SARS-CoV-2 in a high-throughput manner, in this paper, we use different machine learning (ML) model to predict the possible inhibitory synthetic antibodies for SARS-CoV-2. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, like XGBoost, Random Forest, Multilayered Perceptron, Support Vector Machine and Logistic Regression, we screened thousands of hypothetical antibody sequences and found nine stable antibodies that potentially inhibit SARS-CoV-2. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit SARS-CoV-2.

scholarly journals Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning

2020 ◽  
Author(s):  
Rishikesh Magar ◽  
Prakarsh Yadav ◽  
Amir Barati Farimani
Keyword(s):  
Machine Learning ◽  
Neutralizing Antibodies ◽  
Md Simulations ◽  
Inhibitory Antibody ◽  
Clinical Patient ◽  
Corona Virus ◽  
Recent Outbreak ◽  
The Stability ◽  
Novel Coronavirus ◽  
Neutralization Response

AbstractThe fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. Recent outbreak of novel coronavirus infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of COVID-19 will save the life of thousands. In this paper, we devised a machine learning (ML) model to predict the possible inhibitory synthetic antibodies for Corona virus. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, we screened thousands of hypothetical antibody sequences and found 8 stable antibodies that potentially inhibit COVID-19. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit the Corona virus.

Research on enterprise financial economics early warning based on machine learning method

2021 ◽  
pp. 1-11
Author(s):  
Jian Yi
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Financial Economics ◽  
Rapid Development ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Combined Model ◽  
Svm Algorithm ◽  
The Stability

The stability of the economic market is an important factor for the rapid development of the economy, especially for the listed companies, whose financial and economic stability affects the stability of the financial market. It is helpful for the healthy development of enterprises and financial markets to make an accurate early warning of the financial economy of listed enterprises. This paper briefly introduced the support vector machine (SVM) and back-propagation neural network (BPNN) algorithms in the machine learning method. To make up for the defects of the two algorithms, they were combined and applied to the enterprise financial economics early warning. A simulation experiment was carried out on the single SVM algorithm-based, single BPNN algorithm-based, and SVM algorithm and BPNN algorithm combined model with the MATLAB software. The results show that the SVM algorithm and BP algorithm combined model converges faster and has higher precision and recall rate and larger area under the curve (AUC) than the single SVM algorithm-based model and the single BPNN algorithm-based model.

scholarly journals Diagnostic Gene Biomarker Selection for Alzheimer’s Classification using Machine Learning

2019 ◽  
Vol 8 (12) ◽  
pp. 2348-2352
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Search Algorithm ◽  
Memory Loss ◽  
Support Vector ◽  
Gene Expressions ◽  
Gradual Improvement ◽  
Risk Effect ◽  
Multilayered Perceptron ◽  
Differential Gene

Alzheimer’s disease (AD), also referred to as Alzheimer’s is a neurodegenerative disease and most common type of dementia. It starts at an older age and slowly progressive over time. It is a brain disease which causes loss of memory, reasoning and thinking capability of a person. Short-term memory loss is one of the main symptoms of the AD. Other common symptoms are said to be mood-swings, difficulty in understanding language and its interpretation etc. The major problem in the AD is, it can’t be reverted, but controllable with proper treatment. Genetic factors have a high impact on developing an AD, which can be inherited through genes. According to recent studies, gene therapy shows better results for Alzheimer’s patients than other common medications. It reduces the risk effect of the AD and has a gradual improvement on the patient’s condition. So, identification of gene biomarkers, having high involvement in developing AD could improve positive response over the treatment. In this paper, gene expressions of AD patients and normal peoples are analyzed using statistical approaches and Machine Learning (ML) algorithms. Differential Gene Expression (DEG) identification has an important part in the selection of most informative genes. Potential gene biomarkers are selected using a meta-heuristic global optimization algorithm called Rhinoceros Search Algorithm (RSA). As an outcome from RSA, 24 novel gene biomarkers are identified. Four supervised ML algorithms such as Support Vector Machines (SVM), Random Forest (RF), Naïve Bayes (NB) and Multilayered Perceptron Neural Network (MLP-NN) are used for the classification of two different group of samples. Among them, RSA-MLP-NN model achieved 100% accuracy on identifying the distinction between AD and normal genes and proved its efficacy.

scholarly journals Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure

2019 ◽  
Vol 9 (21) ◽  
pp. 4638 ◽  
Author(s):  
Moayedi ◽  
Bui ◽  
Kalantar ◽  
Foong
Keyword(s):  
Machine Learning ◽  
Slope Stability ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Sequential Minimal Optimization ◽  
Stability Assessment ◽  
K Nearest Neighbor ◽  
The Stability ◽  
Intelligent Models ◽  
Improved Support Vector Machine

In this paper, the authors investigated the applicability of combining machine-learning-based models toward slope stability assessment. To do this, several well-known machine-learning-based methods, namely multiple linear regression (MLR), multi-layer perceptron (MLP), radial basis function regression (RBFR), improved support vector machine using sequential minimal optimization algorithm (SMO-SVM), lazy k-nearest neighbor (IBK), random forest (RF), and random tree (RT), were selected to evaluate the stability of a slope through estimating the factor of safety (FOS). In the following, a comparative classification was carried out based on the five stability categories. Based on the respective values of total scores (the summation of scores obtained for the training and testing stages) of 15, 35, 48, 15, 50, 60, and 57, acquired for MLR, MLP, RBFR, SMO-SVM, IBK, RF, and RT, respectively, it was concluded that RF outperformed other intelligent models. The results of statistical indexes also prove the excellent prediction from the optimized structure of the ANN and RF techniques.

scholarly journals Machine Learning Based Prediction of Nanoscale Ice Adhesion on Rough Surfaces

Coatings ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 33
Author(s):  
Simen Ringdahl ◽  
Senbo Xiao ◽  
Jianying He ◽  
Zhiliang Zhang
Keyword(s):  
Machine Learning ◽  
Adhesion Strength ◽  
Liquid Layer ◽  
Interatomic Potential ◽  
Rough Surfaces ◽  
Md Simulations ◽  
Support Vector ◽  
Interatomic Potentials ◽  
Quasi Liquid Layer ◽  
Ice Adhesion

It is widely recognized that surface roughness plays an important role in ice adhesion strength, although the correlation between the two is far from understood. In this paper, two approaches, molecular dynamics (MD) simulations and machine learning (ML), were utilized to study the nanoscale intrinsic ice adhesion strength on rough surfaces. A systematic algorithm for making random rough surfaces was developed and the surfaces were tested for their ice adhesion strength, with varying interatomic potentials. Using MD simulations, the intrinsic ice adhesion strength was found to be significantly lower on rougher surfaces, which was attributed to the lubricating effect of a thin quasi-liquid layer. An increase in the substrate–ice interatomic potential increased the thickness of the quasi-liquid layer on rough surfaces. Two different ML algorithms, regression and classification, were trained using the results from the MD simulations, with support vector machines (SVM) emerging as the best for classifying. The ML approach showed an encouraging prediction accuracy, and for the first time shed light on using ML for anti-icing surface design. The findings provide a better understanding of the role of nanoscale roughness in intrinsic ice adhesion and suggest that ML can be a powerful tool in finding materials with a low ice adhesion strength.

scholarly journals Optimization of the Dewatering Process for Concentrate Pressure Filtering by Support Vector Regression

2021 ◽  
Author(s):  
Huizhon LIU ◽  
Keshun YOU
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Optimization Methods ◽  
Optimization Method ◽  
Filter Press ◽  
Laboratory Simulation ◽  
Support Vector ◽  
Dehydration Process ◽  
Control Variables ◽  
The Stability

Abstract In order to better improve the efficiency of the concentrate filter press dehydration operation, this paper studies the mechanism and optimization methods of the filter press dehydration process. Machine learning models of RBF-OLS, RBF-GRNN and support vector regression (SVR) are constructed respectively, and Perform laboratory simulation and industrial simulation separately. SVR achieves the best accuracy in industrial simulation, the simulated mean relative error (MRE) of moisture and processing capacity are respectively 1.57% and 3.81%. Finally, a simulation model of the filter press dehydration process established by SVR, and the optimtical simulation results Obtained by optimization method based on control variables. The results show that the machine learning method of SVR and optimization methods based on control variables are applied to industry, which can not only ensure the stability of expected production indicators, but also shorten the filter press dehydration cycle to less than 85% of the original.

scholarly journals Newly designed analogues from SARS-CoV inhibitors mimicking the druggable properties against SARS-CoV-2 and its novel variants

2021 ◽  
Author(s):  
Nadim Ferdous ◽  
Mahjerin Nasrin Reza ◽  
Md. Shariful Islam ◽  
Md. Tabassum Hossain Emon ◽  
Mohamed A. Nassan ◽  
...  
Keyword(s):  
Neutralizing Antibodies ◽  
Md Simulations ◽  
New Drugs ◽  
Antiviral Compounds ◽  
Main Protease ◽  
Novel Variants ◽  
Poisson Boltzmann ◽  
The Stability

Abstract The emerging variants of SARS Coronavirus-2 (SARS-CoV-2) has been continuously spreading all over the world and raised global health concerns. The B.1.1.7 (United Kingdom), P.1 (Brazil), B.1.351 (South Africa) and B.1.617 (India) variants resulted due to multiple mutations in the spike glycoprotein (SGp), are resistant to neutralizing antibodies and enable increased transmission. Hence, new drugs might be of great importance against the novel variants of SARS-CoV-2. The SGp and main protease (Mpro) of SARS-CoV-2 are important targets to design and develop antiviral compounds for new drug discovery. In this study, we selected seventeen phytochemicals and later performed molecular docking to determine the binding interactions of the compounds with the two receptors and calculated several drug likeliness properties for each compound. Luteolin, myricetin and quercetin demonstrated higher affinity for both the proteins and interacted efficiently. To get better compounds, we designed three analogues from these compounds and showed the greater druggable properties than the parent compounds. Furthermore, we found that the analogues bind to the residues of both proteins including the recent novel variants of SARS-CoV-2. The binding study was further verified by molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) approaches by assessing the stability of the complexes. MD simulations revealed that Arg457 of SGp and Met49 of Mpro are the most important residues that interacted with the designed inhibitors. Our analysis may give some breakthroughs to develop new therapeutics to treat the proliferation of SARS-CoV-2 in vitro and in vivo.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

Scalable Approach to High Coverages on Oxides via Iterative Training of a Machine-Learning Algorithm

2019 ◽  
Author(s):  
Andrew Medford ◽  
Shengchun Yang ◽  
Fuzhu Liu
Keyword(s):  
Machine Learning ◽  
Chemical Potential ◽  
Learning Algorithm ◽  
Absolute Error ◽  
Low Energy ◽  
Training Data ◽  
High Coverage ◽  
Metal Compounds ◽  
Adsorption Energies ◽  
The Stability

Understanding the interaction of multiple types of adsorbate molecules on solid surfaces is crucial to establishing the stability of catalysts under various chemical environments. Computational studies on the high coverage and mixed coverages of reaction intermediates are still challenging, especially for transition-metal compounds. In this work, we present a framework to predict differential adsorption energies and identify low-energy structures under high- and mixed-adsorbate coverages on oxide materials. The approach uses Gaussian process machine-learning models with quantified uncertainty in conjunction with an iterative training algorithm to actively identify the training set. The framework is demonstrated for the mixed adsorption of CH<sub>x</sub>, NH<sub>x</sub> and OH<sub>x</sub> species on the oxygen vacancy and pristine rutile TiO<sub>2</sub>(110) surface sites. The results indicate that the proposed algorithm is highly efficient at identifying the most valuable training data, and is able to predict differential adsorption energies with a mean absolute error of ~0.3 eV based on <25% of the total DFT data. The algorithm is also used to identify 76% of the low-energy structures based on <30% of the total DFT data, enabling construction of surface phase diagrams that account for high and mixed coverage as a function of the chemical potential of C, H, O, and N. Furthermore, the computational scaling indicates the algorithm scales nearly linearly (N<sup>1.12</sup>) as the number of adsorbates increases. This framework can be directly extended to metals, metal oxides, and other materials, providing a practical route toward the investigation of the behavior of catalysts under high-coverage conditions.

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