scholarly journals pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

2017 ◽  
Vol 09 (09) ◽  
pp. 330-349 ◽  
Author(s):  
Xuan Xiao ◽  
Xiang Cheng ◽  
Shengchao Su ◽  
Qi Mao ◽  
Kuo-Chen Chou
Keyword(s):  
Gene Ontology ◽  
Subcellular Localization ◽  
Gram Positive ◽  
Bacterial Proteins ◽  
Gene Ontology Information

scholarly journals Use of Chou’s 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Hafida Bouziane ◽  
Abdallah Chouarfia
Keyword(s):  
Gene Ontology ◽  
Subcellular Localization ◽  
Protein Interactions ◽  
Protein Function ◽  
Large Scale ◽  
Biological Databases ◽  
Gram Positive ◽  
Essential Information ◽  
Gram Negative ◽  
Bacterial Proteins

AbstractTo date, many proteins generated by large-scale genome sequencing projects are still uncharacterized and subject to intensive investigations by both experimental and computational means. Knowledge of protein subcellular localization (SCL) is of key importance for protein function elucidation. However, it remains a challenging task, especially for multiple sites proteins known to shuttle between cell compartments to perform their proper biological functions and proteins which do not have significant homology to proteins of known subcellular locations. Due to their low-cost and reasonable accuracy, machine learning-based methods have gained much attention in this context with the availability of a plethora of biological databases and annotated proteins for analysis and benchmarking. Various predictive models have been proposed to tackle the SCL problem, using different protein sequence features pertaining to the subcellular localization, however, the overwhelming majority of them focuses on single localization and cover very limited cellular locations. The prediction was basically established on sorting signals, amino acids compositions, and homology. To improve the prediction quality, focus is actually on knowledge information extracted from annotation databases, such as protein–protein interactions and Gene Ontology (GO) functional domains annotation which has been recently a widely adopted and essential information for learning systems. To deal with such problem, in the present study, we considered SCL prediction task as a multi-label learning problem and tried to label both single site and multiple sites unannotated bacterial protein sequences by mining proteins homology relationships using both GO terms of protein homologs and PSI-BLAST profiles. The experiments using 5-fold cross-validation tests on the benchmark datasets showed a significant improvement on the results obtained by the proposed consensus multi-label prediction model which discriminates six compartments for Gram-negative and five compartments for Gram-positive bacterial proteins.

scholarly journals pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC

Genomics ◽  
2019 ◽  
Vol 111 (4) ◽  
pp. 886-892 ◽  
Author(s):  
Xuan Xiao ◽  
Xiang Cheng ◽  
Genqiang Chen ◽  
Qi Mao ◽  
Kuo-Chen Chou
Keyword(s):  
Subcellular Localization ◽  
Training Dataset ◽  
Gram Positive ◽  
Bacterial Proteins
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