Malsite-Deep: Prediction of protein malonylation sites through deep learning and multi-information fusion based on NearMiss-2 strategy

作者：

Highlights：

• A novel method (Malsite-Deep) is used to predict malonylation sites.

• The BE, PWAA, BPB, EAAC, DC, EBGW and BLOSUM62 methods are fused to extract protein sequence features information.

• NearMiss-2 algorithm is used to process the category imbalance data for the first time.

• We firstly build a new deep learning framework which combines the GRU and DNN to predict the malonylation sites.

摘要

•A novel method (Malsite-Deep) is used to predict malonylation sites.•The BE, PWAA, BPB, EAAC, DC, EBGW and BLOSUM62 methods are fused to extract protein sequence features information.•NearMiss-2 algorithm is used to process the category imbalance data for the first time.•We firstly build a new deep learning framework which combines the GRU and DNN to predict the malonylation sites.

论文关键词：Malonylation,Multi-information fusion,NearMiss-2,Gated recurrent units,Deep neural networks

论文评审过程：Received 9 January 2021, Revised 15 December 2021, Accepted 7 January 2022, Available online 13 January 2022, Version of Record 31 January 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.108191