iFeature: A python package and web server for features extraction and selection from protein and peptide sequences. iFeature is a comprehensive Python-based toolkit for generating various numerical feature representation schemes from protein or peptide sequences. iFeature is capable of calculating and extracting a wide spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. Furthermore, iFeature also integrates five kinds of frequently used feature clustering algorithms, four feature selection algorithms and three dimensionality reduction algorithms.
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References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Jia, Jianhua; Li, Xiaoyan; Qiu, Wangren; Xiao, Xuan; Chou, Kuo-Chen: iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC (2019)
- Pan, Yi; Wang, Shiyuan; Zhang, Qi; Lu, Qianzi; Su, Dongqing; Zuo, Yongchun; Yang, Lei: Analysis and prediction of animal toxins by various Chou’s pseudo components and reduced amino acid compositions (2019)
- Shen, Yinan; Tang, Jijun; Guo, Fei: Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou’s general PseAAC (2019)
- Zhao, Wei; Li, Guang-Ping; Wang, Jun; Zhou, Yuan-Ke; Gao, Yang; Du, Pu-Feng: Predicting protein sub-Golgi locations by combining functional domain enrichment scores with pseudo-amino acid compositions (2019)
- Cheng, Xiang; Xiao, Xuan; Chou, Kuo-Chen: pLoc_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC (2018)