(Q38499799)

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Predicting protein submitochondria locations by combining different descriptors into the general form of Chou's pseudo amino acid composition

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scholarly article

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Europe PubMed Central

PubMed publication ID

3 September 2017

Predicting protein submitochondria locations by combining different descriptors into the general form of Chou's pseudo amino acid composition (English)

1 reference

Europe PubMed Central

PubMed publication ID

3 September 2017

author name string

Guo-Liang Fan

1

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Europe PubMed Central

PubMed publication ID

3 September 2017

Qian-Zhong Li

2

1 reference

Europe PubMed Central

PubMed publication ID

3 September 2017

publication date

20 November 2011

1 reference

Europe PubMed Central

PubMed publication ID

3 September 2017

1 reference

Europe PubMed Central

PubMed publication ID

3 September 2017

43

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Europe PubMed Central

PubMed publication ID

3 September 2017

2

1 reference

Europe PubMed Central

PubMed publication ID

3 September 2017

545-555

1 reference

Europe PubMed Central

PubMed publication ID

3 September 2017

https://scigraph.springernature.com/pub.10.1007/s00726-011-1143-4

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ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST.

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Knowledge-based virtual screening of HLA-A*0201-restricted CD8+ T-cell epitope peptides from herpes simplex virus genome

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Using neural networks for prediction of subcellular location of prokaryotic and eukaryotic proteins.

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Nearest neighbour algorithm for predicting protein subcellular location by combining functional domain composition and pseudo-amino acid composition.

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Support vector machines for prediction of protein subcellular location.

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Support Vector Machines for predicting HIV protease cleavage sites in protein.

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Support vector machines for predicting the specificity of GalNAc-transferase.

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Prediction of protein structural classes by support vector machines.

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Support vector machines for prediction of protein signal sequences and their cleavage sites

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Support Vector Machines for Predicting Membrane Protein Types by Using Functional Domain Composition

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Support vector machine for predicting alpha-turn types.

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Identify catalytic triads of serine hydrolases by support vector machines

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Application of SVM to predict membrane protein types

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Predicting subcellular location of proteins using integrated-algorithm method

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Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition

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Prediction of the subcellular location of apoptosis proteins.

1 reference

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21 January 2018

Prediction of protein secondary structure content by using the concept of Chou's pseudo amino acid composition and support vector machine

1 reference

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Prediction of protein cellular attributes using pseudo-amino acid composition

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Some remarks on protein attribute prediction and pseudo amino acid composition

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

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Prediction of protein subcellular locations by GO-FunD-PseAA predictor

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Using GO-PseAA predictor to identify membrane proteins and their types

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

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Predicting protein subcellular location by fusing multiple classifiers

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Recent progress in protein subcellular location prediction

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

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

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Prediction of Protein Structural Classes

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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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MitoProteome: mitochondrial protein sequence database and annotation system

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Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network.

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Prediction of cell wall lytic enzymes using Chou's amphiphilic pseudo amino acid composition

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Identify Golgi protein types with modified Mahalanobis discriminant algorithm and pseudo amino acid composition.

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Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence

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https://api.crossref.org/works/10.1007%2FS00726-011-1143-4

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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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Improving subcellular localization prediction using text classification and the gene ontology

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Improving discrimination of outer membrane proteins by fusing different forms of pseudo amino acid composition.

1 reference

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Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition.

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Prediction of G-protein-coupled receptor classes in low homology using Chou's pseudo amino acid composition with approximate entropy and hydrophobicity patterns

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Prediction of subcellular location apoptosis proteins with ensemble classifier and feature selection

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Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition.

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Using pseudo amino acid composition to predict protease families by incorporating a series of protein biological features.

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ProLoc-GO: utilizing informative Gene Ontology terms for sequence-based prediction of protein subcellular localization

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The role of mitochondria in ischemia/reperfusion injury in organ transplantation

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Using the concept of Chou's pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy

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Using Chou's pseudo amino acid composition based on approximate entropy and an ensemble of AdaBoost classifiers to predict protein subnuclear location

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Characteristic peptides of protein secondary structural motifs

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SVMCRYS: an SVM approach for the prediction of protein crystallization propensity from protein sequence

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AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties.

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Protein networks markedly improve prediction of subcellular localization in multiple eukaryotic species

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Predicting protein subcellular location using Chou's pseudo amino acid composition and improved hybrid approach

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Using pseudo amino acid composition to predict protein subnuclear location with improved hybrid approach.

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The prediction of the structural class of protein: application of the measure of diversity.

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Clustering of highly homologous sequences to reduce the size of large protein databases

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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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Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition.

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Predicting subcellular localization of mycobacterial proteins by using Chou's pseudo amino acid composition.

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Prediction of Subcellular Location of Apoptosis Proteins Using Pseudo Amino Acid Composition: An Approach from Auto Covariance Transformation

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Protein structural class identification directly from NMR spectra using averaged chemical shifts

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Prediction of cyclin proteins using Chou's pseudo amino acid composition

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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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Better prediction of sub-cellular localization by combining evolutionary and structural information.

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Genetic programming for creating Chou's pseudo amino acid based features for submitochondria localization

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High performance set of PseAAC and sequence based descriptors for protein classification.

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Porter: a new, accurate server for protein secondary structure prediction

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Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information

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Using neural networks for prediction of the subcellular location of proteins

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An empirical correlation between secondary structure content and averaged chemical shifts in proteins

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SecretP: Identifying bacterial secreted proteins by fusing new features into Chou’s pseudo-amino acid composition

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Prediction of protein submitochondria locations based on data fusion of various features of sequences

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Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach

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Predicting the cofactors of oxidoreductases based on amino acid composition distribution and Chou's amphiphilic pseudo-amino acid composition

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Protein secondary structure prediction using NMR chemical shift data

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An intriguing controversy over protein structural class prediction

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Some insights into protein structural class prediction

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Subcellular location prediction of apoptosis proteins

1 reference

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

1 reference

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Support vector machines for the classification and prediction of ?-turn types

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Using Functional Domain Composition and Support Vector Machines for Prediction of Protein Subcellular Location

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Identifiers

10.1007/S00726-011-1143-4

1 reference

Europe PubMed Central

PubMed publication ID

3 September 2017

PubMed publication ID

1 reference

Europe PubMed Central

PubMed publication ID

3 September 2017

ResearchGate publication ID

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