Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data

Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data

Abstract Background In recent years, research in artificial neural networks has resurged, now under the deep-learning umbrella, and grown extremely popular. Recently reported success of DL techniques in crowd-sourced QSAR and predictive toxicology competitions has showcased these methods as powerful...

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Bibliographic Details
Main Authors: Alexios Koutsoukas, Keith J. Monaghan, Xiaoli Li, Jun Huan
Format: Article
Language:English
Published: BMC 2017-06-01
Series:Journal of Cheminformatics
Subjects:
Deep learning
SARs
Cheminformatics
Machine-learning
Data-mining
Random forest
Online Access:http://link.springer.com/article/10.1186/s13321-017-0226-y

Internet

http://link.springer.com/article/10.1186/s13321-017-0226-y

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