Ensemble Learning Methods for Binary Classification with Multi-modality within the Classes

2015 ◽  
Author(s):  
Anuj Karpatne ◽  
Ankush Khandelwal ◽  
Vipin Kumar
Keyword(s):  
Ensemble Learning ◽  
Binary Classification ◽  
Learning Methods

An extensive empirical comparison of ensemble learning methods for binary classification

2016 ◽  
Vol 19 (4) ◽  
pp. 1093-1128 ◽  
Author(s):  
Anil Narassiguin ◽  
Mohamed Bibimoune ◽  
Haytham Elghazel ◽  
Alex Aussem
Keyword(s):  
Ensemble Learning ◽  
Binary Classification ◽  
Empirical Comparison ◽  
Learning Methods

Prediction of hERG potassium channel blockage using ensemble learning methods and molecular fingerprints

2020 ◽  
Vol 332 ◽  
pp. 88-96 ◽  
Author(s):  
Miao Liu ◽  
Li Zhang ◽  
Shimeng Li ◽  
Tianzhou Yang ◽  
Lili Liu ◽  
...  
Keyword(s):  
Potassium Channel ◽  
Ensemble Learning ◽  
Learning Methods ◽  
Molecular Fingerprints ◽  
Channel Blockage

scholarly journals Software Effort Prediction Using Ensemble Learning Methods

2020 ◽  
Vol 13 (07) ◽  
pp. 143-160
Author(s):  
Omar H. Alhazmi ◽  
Mohammed Zubair Khan
Keyword(s):  
Ensemble Learning ◽  
Learning Methods ◽  
Effort Prediction

Prediction of Compound-Protein Interactions with Machine Learning Methods

2011 ◽  
pp. 304-317
Author(s):  
Yoshihiro Yamanishi ◽  
Hisashi Kashima
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Chemical Structure ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Binary Classification ◽  
Biological Data ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

In silico prediction of compound-protein interactions from heterogeneous biological data is critical in the process of drug development. In this chapter the authors review several supervised machine learning methods to predict unknown compound-protein interactions from chemical structure and genomic sequence information simultaneously. The authors review several kernel-based algorithms from two different viewpoints: binary classification and dimension reduction. In the results, they demonstrate the usefulness of the methods on the prediction of drug-target interactions and ligand-protein interactions from chemical structure data and genomic sequence data.

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