isGPT: An optimized model to identify sub-Golgi protein types using SVM and Random Forest based feature selection
作者：
Highlights：
• Classification and regression analysis for sub-Golgi protein types.
• Protein representation using n-grams, gapped dipeptides, position specific n-grams.
• Feature selection using importance score provided by Random Forest model.
• Prediction model built using SVM (linear kernel).
• No dependency on evolutionary information of proteins.
• Fast and accurate predictor.
摘要
•Classification and regression analysis for sub-Golgi protein types.•Protein representation using n-grams, gapped dipeptides, position specific n-grams.•Feature selection using importance score provided by Random Forest model.•Prediction model built using SVM (linear kernel).•No dependency on evolutionary information of proteins.•Fast and accurate predictor.
论文关键词：Sub-Golgi Apparatus,Classification,Regression,Support vector machine,Random Forest
论文评审过程：Received 5 October 2017, Revised 13 November 2017, Accepted 17 November 2017, Available online 26 November 2017, Version of Record 5 February 2018.
论文官网地址：https://doi.org/10.1016/j.artmed.2017.11.003
