Prediction of protein–protein interactions based on elastic net and deep forest
作者：
Highlights：
• A novel method (GcForest-PPI) to predict protein–protein interactions.
• The PseAAC, AD, MMI, CTD, AAC-PSSM and DPC-PSSM are fused to extract feature information.
• The elastic net is employed to eliminate redundant and irrelevant features.
• We firstly use deep forest as classifier to predict PPIs via layer-by-layer processing of raw features.
• GcForest-PPI model has good generalization ability on cross-species datasets and PPIs network.
摘要
•A novel method (GcForest-PPI) to predict protein–protein interactions.•The PseAAC, AD, MMI, CTD, AAC-PSSM and DPC-PSSM are fused to extract feature information.•The elastic net is employed to eliminate redundant and irrelevant features.•We firstly use deep forest as classifier to predict PPIs via layer-by-layer processing of raw features.•GcForest-PPI model has good generalization ability on cross-species datasets and PPIs network.
论文关键词：Protein-protein interactions,Multi-information fusion,Elastic net,Deep forest
论文评审过程：Received 1 August 2019, Revised 22 February 2021, Accepted 3 March 2021, Available online 10 March 2021, Version of Record 25 March 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.114876
