An Improved Process for Generating Uniform PSSMs and Its Application in Protein Subcellular Localization via Various Global Dimension Reduction Techniques
This paper proposes an improved protein feature expression called segmented amino acid composition in position-specific scoring matrix (PSSM-SAA) in the field of subcellular localization prediction. Since there has been sufficient local information in the PSSM-SAA vector with high dimensionality, fo...
Main Authors: | Shunfang Wang, Wenjia Li, Yu Fei, Zicheng Cao, Dongshu Xu, Huanyu Guo |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8675739/ |
Similar Items
-
Protein Subcellular Localization with Gaussian Kernel Discriminant Analysis and Its Kernel Parameter Selection
by: Shunfang Wang, et al.
Published: (2017-12-01) -
Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA
by: Shunfang Wang, et al.
Published: (2015-12-01) -
A New Method for Recognizing Cytokines Based on Feature Combination and a Support Vector Machine Classifier
by: Zhe Yang, et al.
Published: (2018-08-01) -
lncLocPred: Predicting LncRNA Subcellular Localization Using Multiple Sequence Feature Information
by: Yongxian Fan, et al.
Published: (2020-01-01) -
An efficient computational method for predicting drug-target interactions using weighted extreme learning machine and speed up robot features
by: Ji-Yong An, et al.
Published: (2021-01-01)