Discovery of novel DGAT1 inhibitors by combination of machine learning methods, pharmacophore model and 3D-QSAR model

Molecular Diversity ◽

10.1007/s11030-021-10247-x ◽

2021 ◽

Author(s):

Hui Zhang ◽

Chen Shen ◽

Hong-Rui Zhang ◽

Wen-Xuan Chen ◽

Qing-Qing Luo ◽

...

Keyword(s):

Machine Learning ◽

Pharmacophore Model ◽

3D Qsar ◽

Learning Methods ◽

Machine Learning Methods

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An improvement on the prediction power of the 3D-QSAR CoMFA models using a hybrid of statistical and machine learning methods: a case study on γ‑secretase modulators of Alzheimer’s disease

Medicinal Chemistry Research ◽

10.1007/s00044-017-1828-7 ◽

2017 ◽

Vol 26 (6) ◽

pp. 1184-1200 ◽

Author(s):

Hadi Khani ◽

Mohammad B. Sepehrifar ◽

Shantia Yarahmadian

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

3D Qsar ◽

Learning Methods ◽

Machine Learning Methods

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Advanced Interpretable Machine Learning Methods for Clinical NGS Big Data of Complex Hereditary Diseases

10.3389/978-2-88966-274-6 ◽

2020 ◽

Keyword(s):

Machine Learning ◽

Big Data ◽

Hereditary Diseases ◽

Learning Methods ◽

Machine Learning Methods ◽

Interpretable Machine Learning

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Application of machine learning methods for automatic interpretation of open hole logging data

Neftyanoe khozyaystvo - Oil Industry ◽

10.24887/0028-2448-2020-11-44-47 ◽

2020 ◽

pp. 44-47

Author(s):

M.A. Basyrov ◽

◽

A.V. Akinshin ◽

I.R. Makhmutov ◽

Yu.D. Kantemirov ◽

...

Keyword(s):

Machine Learning ◽

Learning Methods ◽

Machine Learning Methods ◽

Automatic Interpretation ◽

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TESTING PREDICTION ACCURACY OF HDU ADMISSION FOLLOWING HIGH GRADE SEROUS ADVANCED OVARIAN CANCER CYTOREDUCTIVE SURGERY USING MACHINE LEARNING METHODS.

10.26226/morressier.5fa3ee5d55b1fd4cc4dd93d7 ◽

2020 ◽

Author(s):

Alexandros Laios ◽

Angelika Kaufmann ◽

Mohamed Otify ◽

Diederick De Jong ◽

Tim Broadhead ◽

...

Keyword(s):

Machine Learning ◽

Ovarian Cancer ◽

Cytoreductive Surgery ◽

Prediction Accuracy ◽

Advanced Ovarian Cancer ◽

Learning Methods ◽

Machine Learning Methods

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Evolution of Machine Learning Methods for Memography Classification

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i3.499502 ◽

2018 ◽

Vol 6 (3) ◽

pp. 499-502

Author(s):

R. Swathi ◽

◽

R. Seshadri ◽

Keyword(s):

Machine Learning ◽

Learning Methods ◽

Machine Learning Methods

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FORECASTING THREATS AND CHOOSING THE OPTIMAL STRATEGY FOR ENSURING ECONOMIC SECURITY USING MACHINE LEARNING METHODS

Scientific Review Series 1 Economics and Law ◽

10.26653/2076-4650-2019-6-09 ◽

2019 ◽

pp. 115-123

Author(s):

Evgeniy A. Voronin ◽

◽

Igor V. Yushin ◽

Keyword(s):

Machine Learning ◽

Optimal Strategy ◽

Economic Security ◽

Learning Methods ◽

Machine Learning Methods

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A Generalized Approach to Soil Strength Prediction With Machine Learning Methods

10.21236/ada464726 ◽

2006 ◽

Author(s):

Peter M. Semen

Keyword(s):

Machine Learning ◽

Soil Strength ◽

Strength Prediction ◽

Learning Methods ◽

Machine Learning Methods

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Machine Learning Methods for Financial Forecasting: Application to the S&P 500

SSRN Electronic Journal ◽

10.2139/ssrn.2554146 ◽

2006 ◽

Author(s):

Babak Mahdavi Damghani

Keyword(s):

Machine Learning ◽

Financial Forecasting ◽

Learning Methods ◽

Machine Learning Methods

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Can Machine-Learning Methods Predict the Outcome of an NBA Game?

SSRN Electronic Journal ◽

10.2139/ssrn.3208101 ◽

2018 ◽

Author(s):

Nachi Lieder

Keyword(s):

Machine Learning ◽

Learning Methods ◽

Machine Learning Methods

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Forecasting CO2 Emissions from Energy, Manufacturing and Transport Sectors in Pakistan: Statistical Vs. Machine Learning Methods

SSRN Electronic Journal ◽

10.2139/ssrn.3292279 ◽

2017 ◽

Author(s):

Hakeem-Ur- Rehman ◽

Raza Rafique ◽

Mohsin Nasir ◽

Muhammad Adnan Zahid Chudhery

Keyword(s):

Machine Learning ◽

Co2 Emissions ◽

Learning Methods ◽

Machine Learning Methods

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