ChemInform Abstract: Symbolic, Neural, and Bayesian Machine Learning Models for Predicting Carcinogenicity of Chemical Compounds.

ChemInform ◽  
2000 ◽  
Vol 31 (42) ◽  
pp. no-no
Author(s):  
Dennis Bahler ◽  
Brian Stone ◽  
Carol Wellington ◽  
Douglas W. Bristol
Keyword(s):  
Machine Learning ◽  
Chemical Compounds ◽  
Learning Models ◽  
Bayesian Machine Learning ◽  
Machine Learning Models

scholarly journals Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 145564-145576 ◽  
Author(s):  
Amirhosein Mosavi ◽  
Farzaneh Sajedi Hosseini ◽  
Bahram Choubin ◽  
Massoud Goodarzi ◽  
Adrienn A. Dineva
Keyword(s):  
Machine Learning ◽  
Susceptibility Mapping ◽  
Classifier Ensemble ◽  
Groundwater Salinity ◽  
Learning Models ◽  
Bayesian Machine Learning ◽  
Machine Learning Models

scholarly journals Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads

ACS Omega ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 2353-2361 ◽  
Author(s):  
Manu Anantpadma ◽  
Thomas Lane ◽  
Kimberley M. Zorn ◽  
Mary A. Lingerfelt ◽  
Alex M. Clark ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ebola Virus ◽  
Learning Models ◽  
Bayesian Machine Learning ◽  
Machine Learning Models

scholarly journals Machine Learning Models Identify Inhibitors of SARS-CoV-2

2020 ◽  
Author(s):  
Victor O. Gawriljuk ◽  
Phyo Phyo Kyaw Zin ◽  
Daniel H. Foil ◽  
Jean Bernatchez ◽  
Sungjun Beck ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Antiviral Drug ◽  
Drug Repurposing ◽  
Published Data ◽  
Learning Models ◽  
Machine Learning Model ◽  
Bayesian Machine Learning ◽  
Machine Learning Models

AbstractWith the ongoing SARS-CoV-2 pandemic there is an urgent need for the discovery of a treatment for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need and numerous compounds have been selected for in vitro testing by several groups already. These have led to a growing database of molecules with in vitro activity against the virus. Machine learning models can assist drug discovery through prediction of the best compounds based on previously published data. Herein we have implemented several machine learning methods to develop predictive models from recent SARS-CoV-2 in vitro inhibition data and used them to prioritize additional FDA approved compounds for in vitro testing selected from our in-house compound library. From the compounds predicted with a Bayesian machine learning model, CPI1062 and CPI1155 showed antiviral activity in HeLa-ACE2 cell-based assays and represent potential repurposing opportunities for COVID-19. This approach can be greatly expanded to exhaustively virtually screen available molecules with predicted activity against this virus as well as a prioritization tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for SARS-CoV-2 is available at www.assaycentral.org.

scholarly journals Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery

PLoS ONE ◽  
2015 ◽  
Vol 10 (10) ◽  
pp. e0141076 ◽  
Author(s):  
Sean Ekins ◽  
Peter B. Madrid ◽  
Malabika Sarker ◽  
Shao-Gang Li ◽  
Nisha Mittal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mycobacterium Tuberculosis ◽  
Drug Discovery ◽  
Learning Models ◽  
Bayesian Machine Learning ◽  
Machine Learning Models

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

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