pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC. (Q38431305)

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scientific article published on 12 July 2017

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English	pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC.	scientific article published on 12 July 2017

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scholarly article

1 reference

Europe PubMed Central

3 September 2017

pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC. (English)

1 reference

Europe PubMed Central

3 September 2017

2

object named as

Xuan Xiao

1 reference

Europe PubMed Central

3 September 2017

3

object named as

Kuo-Chen Chou

1 reference

Europe PubMed Central

3 September 2017

author name string

Xiang Cheng

1

1 reference

Europe PubMed Central

3 September 2017

language of work or name

1 reference

based on heuristic

inferred from title

publication date

12 July 2017

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Europe PubMed Central

3 September 2017

Molecular BioSystems

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Europe PubMed Central

3 September 2017

13

1 reference

Europe PubMed Central

3 September 2017

9

1 reference

Europe PubMed Central

3 September 2017

1722-1727

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Europe PubMed Central

3 September 2017

Recent progress in protein subcellular location prediction

1 reference

https://api.crossref.org/works/10.1039%2FC7MB00267J

21 January 2018

Automated subcellular location determination and high-throughput microscopy

1 reference

https://api.crossref.org/works/10.1039%2FC7MB00267J

21 January 2018

Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localization

1 reference

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21 January 2018

iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites.

1 reference

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21 January 2018

Some remarks on predicting multi-label attributes in molecular biosystems

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Some remarks on protein attribute prediction and pseudo amino acid composition

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A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0

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iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites.

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iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins.

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A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0

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iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins

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dRHP-PseRA: detecting remote homology proteins using profile-based pseudo protein sequence and rank aggregation

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iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types.

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pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach.

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Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes

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The modified Mahalanobis Discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition.

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Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses

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Prediction of GABAA receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine

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Identifying bacterial virulent proteins by fusing a set of classifiers based on variants of Chou's pseudo amino acid composition and on evolutionary information

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Predict subcellular locations of singleplex and multiplex proteins by semi-supervised learning and dimension-reducing general mode of Chou's PseAAC.

1 reference

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21 January 2018

Chou's pseudo amino acid composition improves sequence-based antifreeze protein prediction

1 reference

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Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC.

1 reference

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21 January 2018

Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC.

1 reference

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21 January 2018

OOgenesis_Pred: A sequence-based method for predicting oogenesis proteins by six different modes of Chou's pseudo amino acid composition

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iPGK-PseAAC: Identify Lysine Phosphoglycerylation Sites in Proteins by Incorporating Four Different Tiers of Amino Acid Pairwise Coupling Information into the General PseAAC.

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iPreny-PseAAC: identify C-terminal cysteine prenylation sites in proteins by incorporating two tiers of sequence couplings into PseAAC.

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21 January 2018

Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences

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An Unprecedented Revolution in Medicinal Chemistry Driven by the Progress of Biological Science

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Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences

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iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites

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A multi-label classifier for predicting the subcellular localization of gram-negative bacterial proteins with both single and multiple sites

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GOASVM: a subcellular location predictor by incorporating term-frequency gene ontology into the general form of Chou's pseudo-amino acid composition.

1 reference

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21 January 2018

Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms

1 reference

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21 January 2018

Hum-mPLoc: an ensemble classifier for large-scale human protein subcellular location prediction by incorporating samples with multiple sites.

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21 January 2018

A new hybrid approach to predict subcellular localization of proteins by incorporating gene ontology

1 reference

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Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-Nearest Neighbor classifiers

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Predicting membrane protein types by the LLDA algorithm

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Prediction of protein structural classes

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iPTM-mLys: identifying multiple lysine PTM sites and their different types

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Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis.

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Some insights into protein structural class prediction

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MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM.

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Classification of membrane protein types using Voting Feature Interval in combination with Chou's Pseudo Amino Acid Composition

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Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou's general PseAAC.

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iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC.

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Identifiers

10.1039/C7MB00267J

1 reference

Europe PubMed Central

3 September 2017

1 reference

Europe PubMed Central

3 September 2017

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