VAERHNN: Voting-averaged ensemble regression and hybrid neural network to investigate potent leads against colorectal cancer

作者：

Highlights：

• We propose an AI-based DR protocol, VAERHNN, to investigate potent leads against CRC.

• We built VAER based on EL algorithms and HNN consisting of multiple neural networks.

• MD simulations and in vitro assays validated FAD as a potent lead against CRC.

• Our key advance is to propose an AI-based multi-integrated prediction protocol.

摘要

•We propose an AI-based DR protocol, VAERHNN, to investigate potent leads against CRC.•We built VAER based on EL algorithms and HNN consisting of multiple neural networks.•MD simulations and in vitro assays validated FAD as a potent lead against CRC.•Our key advance is to propose an AI-based multi-integrated prediction protocol.

论文关键词：Colorectal cancer,Ensemble learning,Hybrid neural network,Molecular dynamics simulation,In vitro assay

论文评审过程：Received 29 May 2022, Revised 4 September 2022, Accepted 17 September 2022, Available online 23 September 2022, Version of Record 11 October 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.109925