scholarly journals PlantLoc: an accurate web server for predicting plant protein subcellular localization by substantiality motif

2013 ◽  
Vol 41 (W1) ◽  
pp. W441-W447 ◽  
Author(s):  
Shengnan Tang ◽  
Tonghua Li ◽  
Peisheng Cong ◽  
Wenwei Xiong ◽  
Zhiheng Wang ◽  
...  
Keyword(s):  
Subcellular Localization ◽  
Web Server ◽  
Plant Protein ◽  
Protein Subcellular Localization

scholarly journals Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization

Life ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 293
Author(s):  
Warin Wattanapornprom ◽  
Chinae Thammarongtham ◽  
Apiradee Hongsthong ◽  
Supatcha Lertampaiporn
Keyword(s):  
Subcellular Localization ◽  
Protein Localization ◽  
Imbalanced Data ◽  
Plant Protein ◽  
Feature Subset ◽  
Protein Subcellular Localization ◽  
Multiple Classifiers ◽  
Testing Dataset ◽  
Ensemble Machine Learning ◽  
Final Probability

The accurate prediction of protein localization is a critical step in any functional genome annotation process. This paper proposes an improved strategy for protein subcellular localization prediction in plants based on multiple classifiers, to improve prediction results in terms of both accuracy and reliability. The prediction of plant protein subcellular localization is challenging because the underlying problem is not only a multiclass, but also a multilabel problem. Generally, plant proteins can be found in 10–14 locations/compartments. The number of proteins in some compartments (nucleus, cytoplasm, and mitochondria) is generally much greater than that in other compartments (vacuole, peroxisome, Golgi, and cell wall). Therefore, the problem of imbalanced data usually arises. Therefore, we propose an ensemble machine learning method based on average voting among heterogeneous classifiers. We first extracted various types of features suitable for each type of protein localization to form a total of 479 feature spaces. Then, feature selection methods were used to reduce the dimensions of the features into smaller informative feature subsets. This reduced feature subset was then used to train/build three different individual models. In the process of combining the three distinct classifier models, we used an average voting approach to combine the results of these three different classifiers that we constructed to return the final probability prediction. The method could predict subcellular localizations in both single- and multilabel locations, based on the voting probability. Experimental results indicated that the proposed ensemble method could achieve correct classification with an overall accuracy of 84.58% for 11 compartments, on the basis of the testing dataset.

scholarly journals Inside or outside? A new collection of Gateway vectors allowing plant protein subcellular localization or over-expression

Plasmid ◽  
2019 ◽  
Vol 105 ◽  
pp. 102436 ◽  
Author(s):  
François Berthold ◽  
David Roujol ◽  
Caroline Hemmer ◽  
Elisabeth Jamet ◽  
Christophe Ritzenthaler ◽  
...  
Keyword(s):  
Subcellular Localization ◽  
Plant Protein ◽  
Protein Subcellular Localization ◽  
Over Expression ◽  
Gateway Vectors

scholarly journals ngLOC: software and web server for predicting protein subcellular localization in prokaryotes and eukaryotes

2012 ◽  
Vol 5 (1) ◽  
pp. 351 ◽  
Author(s):  
Brian R King ◽  
Suleyman Vural ◽  
Sanjit Pandey ◽  
Alex Barteau ◽  
Chittibabu Guda
Keyword(s):  
Subcellular Localization ◽  
Web Server ◽  
Protein Subcellular Localization

An efficient transient assays system using Agrobacterium-mediated transformation of onion (Allium cepa) epidermal cells

2020 ◽  
Vol 80 (03) ◽  
Author(s):  
Yu-Miao Zhang ◽  
Jun Wang ◽  
Tao Wu
Keyword(s):  
Subcellular Localization ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Epidermal Cells ◽  
Cyclin Dependent Kinase ◽  
Protein Subcellular Localization ◽  
Protein Protein Interactions ◽  
Efficient System ◽  
Protein Protein Interaction ◽  
Onion Epidermal Cells

In this study, the Agrobacterium infection medium, infection duration, detergent, and cell density were optimized. The sorghum-based infection medium (SbIM), 10-20 min infection time, addition of 0.01% Silwet L-77, and Agrobacterium optical density at 600 nm (OD600), improved the competence of onion epidermal cells to support Agrobacterium infection at >90% efficiency. Cyclin-dependent kinase D-2 (CDKD-2) and cytochrome c-type biogenesis protein (CYCH), protein-protein interactions were localized. The optimized procedure is a quick and efficient system for examining protein subcellular localization and protein-protein interaction.

Sign in / Sign up

Export Citation Format

Share Document