scholarly journals Classification of RNA backbone conformations into rotamers using 13C′ chemical shifts: exploring how far we can go

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7904 ◽  
Author(s):  
Alejandro A. Icazatti ◽  
Juan M. Loyola ◽  
Igal Szleifer ◽  
Jorge A. Vila ◽  
Osvaldo A. Martin
Keyword(s):  
Machine Learning ◽  
Chemical Shifts ◽  
Conformational Space ◽  
Machine Learning Methods ◽  
Phosphate Backbone ◽  
Ribose Phosphate ◽  
Conformational Preferences ◽  
Rna Backbone ◽  
Torsional Angles

The conformational space of the ribose-phosphate backbone is very complex as it is defined in terms of six torsional angles. To help delimit the RNA backbone conformational preferences, 46 rotamers have been defined in terms of these torsional angles. In the present work, we use the ribose experimental and theoretical 13C′ chemical shifts data and machine learning methods to classify RNA backbone conformations into rotamers and families of rotamers. We show to what extent the experimental 13C′ chemical shifts can be used to identify rotamers and discuss some problem with the theoretical computations of 13C′ chemical shifts.

scholarly journals Classification of RNA backbone conformation into rotamers using 13C′ chemical shifts: How far we can go?

2019 ◽  
Author(s):  
A. A. Icazatti ◽  
J.M. Loyola ◽  
I. Szleifer ◽  
J.A. Vila ◽  
O. A. Martin
Keyword(s):  
Chemical Shifts ◽  
Conformational Space ◽  
Phosphate Backbone ◽  
Ribose Phosphate ◽  
Conformational Preferences ◽  
Backbone Conformation ◽  
Rna Backbone ◽  
Rna Backbone Conformation ◽  
Torsional Angles

ABSTRACTThe conformational space of the ribose–phosphate backbone is very complex as is defined in terms of six torsional angles. To help delimit the RNA backbone conformational preferences 46 rotamers have been defined in terms of the these torsional angles. In the present work, we use the ribose experimental and theoretical 13C′ chemical shifts data and machine learning methods to classify RNA backbone conformations into rotamers and families of rotamers. We show to what extent the use of experimental 13C′ chemical shifts can be used to identify rotamers and discuss some problem with the theoretical computations of 13C′ chemical shifts.

Machine Learning methods for micro-FTIR imaging classification of human skin tumors

2021 ◽  
Author(s):  
Matheus del Valle ◽  
Kleber Stancari ◽  
Pedro Arthur Augusto de Castro ◽  
Moises Oliveira dos Santos ◽  
Denise Maria Zezell
Keyword(s):  
Machine Learning ◽  
Human Skin ◽  
Skin Tumors ◽  
Learning Methods ◽  
Ftir Imaging ◽  
Machine Learning Methods ◽  
Imaging Classification

TRAFFIC FLOWS FORECASTING BASED ON MACHINE LEARNING

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Traffic Flows ◽  
Random Forest Regression ◽  
Machine Learning Methods ◽  
Packet Arrival ◽  
Entire Flow ◽  
The Mean ◽  
Active Flow

The article aims to develop a model for forecasting the characteristics of traffic flows in real-time based on the classification of applications using machine learning methods to ensure the quality of service. It is shown that the model can forecast the mean rate and frequency of packet arrival for the entire flow of each class separately. The prediction is based on information about the previous flows of this class and the first 15 packets of the active flow. Thus, the Random Forest Regression method reduces the prediction error by approximately 1.5 times compared to the standard mean estimate for transmitted packets issued at the switch interface.

scholarly journals Classification of HIV-1 Protease Inhibitors by Machine Learning Methods

ACS Omega ◽  
2018 ◽  
Vol 3 (11) ◽  
pp. 15837-15849 ◽  
Author(s):  
Yang Li ◽  
Yujia Tian ◽  
Zijian Qin ◽  
Aixia Yan
Keyword(s):  
Machine Learning ◽  
Protease Inhibitors ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Hiv 1

scholarly journals Seeing It All: Evaluating Supervised Machine Learning Methods for the Classification of Diverse Otariid Behaviours

PLoS ONE ◽  
2016 ◽  
Vol 11 (12) ◽  
pp. e0166898 ◽  
Author(s):  
Monique A. Ladds ◽  
Adam P. Thompson ◽  
David J. Slip ◽  
David P. Hocking ◽  
Robert G. Harcourt
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals RESEARCH ON BRAIN NETWORK CLASSIFICATION METHOD BASED ON INTEGRATED MODEL

2020 ◽  
pp. 1-2
Author(s):  
Zhang- sensen
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Brain Network ◽  
Classification Model ◽  
Comparative Experiment ◽  
Machine Learning Methods ◽  
Healthy Elderly ◽  
Healthy Elderly People ◽  
The Brain

mild cognitive impairment (MCI) is a condition between healthy elderly people and alzheimer's disease (AD). At present, brain network analysis based on machine learning methods can help diagnose MCI. In this paper, the brain network is divided into several subnets based on the shortest path,and the feature vectors of each subnet are extracted and classified. In order to make full use of subnet information, this paper adopts integrated classification model for classification.Each base classification model can predict the classification of a subnet,and the classification results of all subnets are calculated as the classification results of brain network.In order to verify the effectiveness of this method,a brain network of 66 people was constructed and a comparative experiment was carried out.The experimental results show that the classification accuracy of the integrated classification model proposed in this paper is 19% higher than that of SVM,which effectively improves the classification accuracy

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