Subcellular localization prediction of apoptosis proteins based on evolutionary information and support vector machine
作者：
Highlights：
• The novel evolutionary-conservative information is introduced to represent protein sequences.
• Position-specific scoring matrix is divided into several blocks based on the proportion of golden section.
• Our method provides the state-of-the-art performance for predicting subcellular location of apoptosis proteins.
摘要
•The novel evolutionary-conservative information is introduced to represent protein sequences.•Position-specific scoring matrix is divided into several blocks based on the proportion of golden section.•Our method provides the state-of-the-art performance for predicting subcellular location of apoptosis proteins.
论文关键词：Apoptosis protein,Position-specific scoring matrix,Golden section,Support vector machine
论文评审过程：Received 21 November 2016, Revised 8 May 2017, Accepted 11 May 2017, Available online 24 May 2017, Version of Record 29 May 2017.
论文官网地址：https://doi.org/10.1016/j.artmed.2017.05.007
