Active Learning with Support Vector Machines in the Drug Discovery Process

2003 ◽  
Vol 43 (2) ◽  
pp. 667-673 ◽  
Author(s):  
Manfred K. Warmuth ◽  
Jun Liao ◽  
Gunnar Rätsch ◽  
Michael Mathieson ◽  
Santosh Putta ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Support Vector Machines ◽  
Active Learning ◽  
Support Vector ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Vector Machines

Active Learning with Support Vector Machines in the Drug Discovery Process.

ChemInform ◽  
2003 ◽  
Vol 34 (22) ◽  
Author(s):  
Manfred K. Warmuth ◽  
Jun Liao ◽  
Gunnar Raetsch ◽  
Michael Mathieson ◽  
Santosh Putta ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Support Vector Machines ◽  
Active Learning ◽  
Support Vector ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Vector Machines

Inconsistency-based active learning for support vector machines

2012 ◽  
Vol 45 (10) ◽  
pp. 3751-3767 ◽  
Author(s):  
Ran Wang ◽  
Sam Kwong ◽  
Degang Chen
Keyword(s):  
Support Vector Machines ◽  
Active Learning ◽  
Support Vector ◽  
Vector Machines

scholarly journals Active learning with support vector machines

2014 ◽  
Vol 4 (4) ◽  
pp. 313-326 ◽  
Author(s):  
Jan Kremer ◽  
Kim Steenstrup Pedersen ◽  
Christian Igel
Keyword(s):  
Support Vector Machines ◽  
Active Learning ◽  
Support Vector ◽  
Vector Machines

scholarly journals Virtual Screening of Drug Likeness using Tree Based Ensemble Classifier

2018 ◽  
Vol 11 (3) ◽  
pp. 1513-1519 ◽  
Author(s):  
R. Ani ◽  
Roshini Manohar ◽  
Gayathri Anil ◽  
O.S. Deepa
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Molecular Descriptor ◽  
Computational Procedure ◽  
Ensemble Classifier ◽  
Support Vector ◽  
Classification Algorithms ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Rotation Forest

In earlier years, the Drug discovery process took years to identify and process a Drug. It takes a normal of 12 years for a Drug to travel from the research lab to the patient. With the introduction of Machine Learning in Drug discovery, the whole process turned out to be simple. The utilization of computational tools in the early stages of Drug development has expanded in recent decades. A computational procedure carried out in Drug discovery process is Virtual Screening (VS). VS are used to identify the compounds which can bind to a Drug target. The preliminary process before analyzing the bonding of ligand and drug protein target is the prediction of drug likeness of compounds. The main objective of this study is to predict Drug likeness properties of Drug compounds based on molecular descriptor information using Tree based ensembles. In this study, many classification algorithms are analyzed and the accuracy for the prediction of drug likeness is calculated. The study shows that accuracy of rotation forest outperforms the accuracy of other classification algorithms in the prediction of drug likeness of chemical compounds. The measured accuracies of the Rotation Forest, Random Forest, Support Vector Machines, KNN, Decision Tree and Naïve Bayes are 98%, 97%, 94.8%, 92.8%, 91.4%, 89.5% respectively.

Combining active learning and transductive support vector machines for sea ice detection

2018 ◽  
Vol 12 (02) ◽  
pp. 1 ◽  
Author(s):  
Yanling Han ◽  
Peng Li ◽  
Yun Zhang ◽  
Zhonghua Hong ◽  
Kaichen Liu ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Active Learning ◽  
Sea Ice ◽  
Support Vector ◽  
Vector Machines ◽  
Ice Detection

Active learning support vector machines for optimal sample selection in classification

2004 ◽  
Vol 18 (6) ◽  
pp. 294-305 ◽  
Author(s):  
Simeone Zomer ◽  
Miguel Del Nogal Sánchez ◽  
Richard G. Brereton ◽  
José L. Pérez Pavón
Keyword(s):  
Support Vector Machines ◽  
Active Learning ◽  
Sample Selection ◽  
Support Vector ◽  
Learning Support ◽  
Optimal Sample ◽  
Vector Machines
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