EventsMOL2NET'21, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 7th ed.
Event submissions
Published
with-doi10.3390/mol2net-07-11217
This submission belongs to the session 01. USE.DAT-07: USA-Europe Data Analysis Trends Congress, Cambridge, UK-Bilbao, Basque Country-Miami, USA, 2021 of the event MOL2NET'21, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 7th ed.
Published date
24 Oct, 2021
Academic Editor
author-avatarHumbert G. Díaz
Citation
Bernabe Ortega-Tenezaca, Viviana F. Quevedo-Tumailli, Predictive Modeling with Machine Learning and Perturbation Theory, in Proceedings of MOL2NET'21, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 7th ed., 25 January–30 December 2021, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-07-11217
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Predictive Modeling with Machine Learning and Perturbation Theory

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Bernabe Ortega-Tenezaca 1,2
Viviana F. Quevedo-Tumailli 2,3
1. RNASA-IMEDIR, Computer Science Faculty, University of A Coruña, 15071, A Coruña, Spain., Ecuador
2. Universidad Estatal Amazónica – Puyo, Pastaza, Ecuador.
3. RNASA-IMEDIR, Computer Science Faculty, University of A Coruña, 15071, A Coruña, Spain.
Abstract

PTML is a combination of Machine Learning (ML) and Perturbation Theory (PT) that allows to create prediction models in many areas of knowledge mainly in Medicinal Chemistry to handle large amounts of data representing physical and chemical properties of different organisms and biological systems under different input conditions. PTML allows to establish dispersion measurements on descriptors of physicochemical properties of different organisms with high values of sensitivity, specificity and accuracy higher than 70%

Keywords
PTML
Machine Learning
Modeling
Manuscript
Predictive models_BO_mol2net_2021_04chembioinfo-03.pdf
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