Auto-KPCA: A Two-Step Hybrid Feature Extraction Technique for Quantitative Structure–Activity Relationship Modeling

Auto-KPCA: A Two-Step Hybrid Feature Extraction Technique for Quantitative Structure–Activity Relationship Modeling

Quantitative structure-activity relationship (QSAR) modeling is an established approach for drug discovery, but many QSAR datasets suffer from the curse of dimensionality, a challenge that is usually addressed by using dimensionality reduction techniques such as principal component analysis (PCA). H...

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Bibliographic Details
Main Authors: Shrooq A. Alsenan, Isra M. Al-Turaiki, Alaaeldin M. Hafez
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Autoencoder
deep generalized autoencoder (dGAE)
dimensioanlity reduction
feature extraction
kernel principal component analysis (KPCA)
quantitative structure–activity relation (QSAR)
Online Access:https://ieeexplore.ieee.org/document/9308911/

Internet

https://ieeexplore.ieee.org/document/9308911/

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