scholarly journals Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses

2020 ◽  
Author(s):  
Ayal B. Gussow ◽  
Noam Auslander ◽  
Guilhem Faure ◽  
Yuri I. Wolf ◽  
Feng Zhang ◽  
...  
Keyword(s):  
Nucleocapsid Protein ◽  
Case Fatality ◽  
Machine Learning Techniques ◽  
Human Pathogens ◽  
Host Switch ◽  
Genomic Features ◽  
Nuclear Localization Signals ◽  
Evolutionary Origins ◽  
Learning Techniques ◽  
Human Coronaviruses

AbstractSARS-CoV-2 poses an immediate, major threat to public health across the globe. Here we report an in-depth molecular analysis to reconstruct the evolutionary origins of the enhanced pathogenicity of SARS-CoV-2 and other coronaviruses that are severe human pathogens. Using integrated comparative genomics and machine learning techniques, we identify key genomic features that differentiate SARS-CoV-2 and the viruses behind the two previous deadly coronavirus outbreaks, SARS-CoV and MERS-CoV, from less pathogenic coronaviruses. These features include enhancement of the nuclear localization signals in the nucleocapsid protein and distinct inserts in the spike glycoprotein that appear to be associated with high case fatality rate of these coronaviruses as well as the host switch from animals to humans. The identified features could be crucial elements of coronavirus pathogenicity and possible targets for diagnostics, prognostication and interventions.

scholarly journals Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses

2020 ◽  
Vol 117 (26) ◽  
pp. 15193-15199 ◽  
Author(s):  
Ayal B. Gussow ◽  
Noam Auslander ◽  
Guilhem Faure ◽  
Yuri I. Wolf ◽  
Feng Zhang ◽  
...  
Keyword(s):  
Nucleocapsid Protein ◽  
Case Fatality ◽  
Machine Learning Techniques ◽  
Human Pathogens ◽  
Host Switch ◽  
Genomic Features ◽  
Nuclear Localization Signals ◽  
Evolutionary Origins ◽  
Learning Techniques ◽  
Human Coronaviruses

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses an immediate, major threat to public health across the globe. Here we report an in-depth molecular analysis to reconstruct the evolutionary origins of the enhanced pathogenicity of SARS-CoV-2 and other coronaviruses that are severe human pathogens. Using integrated comparative genomics and machine learning techniques, we identify key genomic features that differentiate SARS-CoV-2 and the viruses behind the two previous deadly coronavirus outbreaks, SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), from less pathogenic coronaviruses. These features include enhancement of the nuclear localization signals in the nucleocapsid protein and distinct inserts in the spike glycoprotein that appear to be associated with high case fatality rate of these coronaviruses as well as the host switch from animals to humans. The identified features could be crucial contributors to coronavirus pathogenicity and possible targets for diagnostics, prognostication, and interventions.

scholarly journals The strength of a NES motif in the nucleocapsid protein of human coronaviruses is related to genus, but not to pathogenic capacity

2020 ◽  
Author(s):  
Maria Sendino ◽  
Miren Josu Omaetxebarria ◽  
Jose Antonio Rodriguez
Keyword(s):  
In Silico ◽  
Nuclear Export ◽  
Nucleocapsid Protein ◽  
Severe Disease ◽  
Nuclear Export Signal ◽  
Case Fatality ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Human Coronaviruses ◽  
Export Signal

ABSTRACTSeven members of the Coronaviridae family infect humans, but only three (SARS-CoV, SARS-CoV-2 and MERS-CoV) cause severe disease with a high case fatality rate. Using in silico analyses (machine learning techniques and comparative genomics), several features associated to coronavirus pathogenicity have been recently proposed, including a potential increase in the strength of a predicted novel nuclear export signal (NES) motif in the nucleocapsid protein.Here, we have used a well-established nuclear export assay to experimentally establish whether the recently proposed nucleocapsid NESs are capable of mediating nuclear export, and to evaluate if their activity correlates with coronavirus pathogenicity.The six NES motifs tested were functional in our assay, but displayed wide differences in export activity. Importantly, these differences in NES strength were not related to strain pathogenicity. Rather, we found that the NESs of the strains belonging to the genus Alphacoronavirus were markedly stronger than the NESs of the strains belonging to the genus Betacoronavirus.We conclude that, while some of the genomic features recently identified in silico could be crucial contributors to coronavirus pathogenicity, this does not appear to be the case of nucleocapsid NES activity, as it is more closely related to coronavirus genus than to pathogenic capacity.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

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.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

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.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

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