Prediction of post-translational modification cross-talk and mutation within proteins via imbalanced learning

作者：

Highlights：

• Using dynamic features based on ENMs to describe PTM cross-talk.

• Determining optimal feature combination after careful feature selection.

• Proposing a sampling algorithm to learn better and predict PTM cross-talk.

• Maintaining fine stability by introducing imbalanced learning.

摘要

•Using dynamic features based on ENMs to describe PTM cross-talk.•Determining optimal feature combination after careful feature selection.•Proposing a sampling algorithm to learn better and predict PTM cross-talk.•Maintaining fine stability by introducing imbalanced learning.

论文关键词：Post-translational modification,Cross-talk,Mutation,Imbalanced learning,Deep learning,Feature selection

论文评审过程：Received 25 January 2022, Revised 5 August 2022, Accepted 13 August 2022, Available online 23 August 2022, Version of Record 31 August 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118593