Prediction of Compound Profiling Matrices, Part II: Relative Performance of Multitask Deep Learning and Random Forest Classification on the Basis of Varying Amounts of Training Data
Main Authors: | Raquel Rodríguez-Pérez, Jürgen Bajorath |
---|---|
Format: | Article |
Language: | English |
Published: |
American Chemical Society
2018-09-01
|
Series: | ACS Omega |
Online Access: | http://dx.doi.org/10.1021/acsomega.8b01682 |
Similar Items
-
Multitask Machine Learning for Classifying Highly and Weakly Potent Kinase Inhibitors
by: Raquel Rodríguez-Pérez, et al.
Published: (2019-02-01) -
Extracting Compound Profiling Matrices from Screening Data
by: Martin Vogt, et al.
Published: (2018-04-01) -
LAND USE CLASSIFICATION USING DEEP MULTITASK NETWORKS
by: J. R. Bergado, et al.
Published: (2020-08-01) -
Prediction of Compound Profiling Matrices Using Machine Learning
by: Raquel Rodríguez-Pérez, et al.
Published: (2018-04-01) -
Support Vector Machine Classification and Regression Prioritize Different Structural Features for Binary Compound Activity and Potency Value Prediction
by: Raquel Rodríguez-Pérez, et al.
Published: (2017-10-01)