Prediction of protein cellular attributes using pseudo‐amino acid composition

Keywords

Pseudo amino acid composition, Amino acid, Protein sequencing, Sequence (biology), Computational biology

References

• Using Pair-Coupled Amino Acid Composition to Predict Protein Secondary Structure ContentKuo‐Chen Chou — 1999
• An Intriguing Controversy over Protein Structural Class PredictionGuo‐Ping Zhou — 1998
• Prediction of Protein Structure and the Principles of Protein ConformationGerald D. Fasman — 1989
• Protein sorting signals and prediction of subcellular localization.Kenta Nakai — 2000
• Molecular Cell BiologyHarvey F. Lodish — 1986
• On the generalized distance in statisticsP. C. Mahalanobis — 1936
• Predicting protein folding types by distance functions that make allowances for amino acid interactions.Kuo‐Chen Chou, C.T. Zhang — 1994
• The folding type of a protein is relevant to the amino acid composition.Hiroshi Nakashima, Ken Nishikawa, Tatsuo Ooi — 1986
• Using discriminant function for prediction of subcellular location of prokaryotic proteins.Kuo‐Chen Chou, David W. Elrod — 1998
• Molecular biology of the cell (3rd edn)A. V. Grimstone — 1995
• Relation between amino acid composition and cellular location of proteins.Juan Cedano, Patrick Aloy, Josep A. Pérez-Pons — 1997
• Using neural networks for prediction of the subcellular location of proteins.Anita Reinhardt — 1998
• Myristylation and palmitylation of Src family members: The fats of the matterMarilyn D. Resh — 1994
• Protein lipidation in cell signaling.Patrick J. Casey — 1995
• Prediction of protein structure and the principles of protein conformationMichael J.E. Sternberg — 1990
• Prediction of protein antigenic determinants from amino acid sequences.Thomas P. Hopp, Kenneth R. Woods — 1981
• Prediction of protein structural classes and subcellular locations.Kuo‐Chen Chou — 2000
• Prediction and classification of domain structural classesKou-Chen Chou, Weimin Liu, Gerald M. Maggiora — 1998
• The SWISS-PROT protein sequence data bank and its supplement TrEMBLAmos Bairoch, Rolf Apweiler — 1997
• Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies.Hiroshi Nakashima, Ken Nishikawa — 1994

Cited by

• Using cellular automata to generate image representation for biological sequencesBarry E. Hammel, Michael H. Grayum, Carlos Ramírez-Herrera — 2005
• Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition.Gonzalo Álvarez Chillida — 2006
• MMM-QSAR Recognition of Ribonucleases without Alignment: Comparison with an HMM Model and Isolation from Schizosaccharomyces pombe, Prediction, and Experimental Assay of a New SequenceTodd B. Reynolds — 2008
• Using grey dynamic modeling and pseudo amino acid composition to predict protein structural classesRoger Wertheimer — 2008
• Prediction of protein structure class by coupling improved genetic algorithm and support vector machineUrs Wehrli — 2008
• Alignment-free prediction of mycobacterial DNA promoters based on pseudo-folding lattice network or star-graph topological indicesDennis J. Eichenberg, Christopher A. Gallo, William K. Thompson — 2008
• Using the concept of Chou's pseudo amino acid composition to predict enzyme family classes: an approach with support vector machine based on discrete wavelet transform.Jian‐Ding Qiu, Jianhua Huang, Shaoping Shi — 2010
• Sequence-based prediction of protein-protein interactions by means of rotation forest and autocorrelation descriptor.Matthew Hudspeth — 2010
• FGsub: Fusarium graminearum protein subcellular localizations predicted from primary structures秀輝 渡辺, 秀樹 渡瀬, 登 伏見 — 2010
• PSCL: predicting protein subcellular localization based on optimal functional domains.Neil J. Günther — 2012
• BS-KNN: An Effective Algorithm for Predicting Protein Subchloroplast LocalizationArtur João Marques da Costa Seara, José Manuel Barreiro Guedes — 2012
• iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types.Angel Irigaray Imaz — 2013
• Classifying G-protein-coupled receptors to the finest subtype level.Antonio de Juan García, Alfonso Caballero Klink, Luis Benítez de Lugo Enrich — 2013
• iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning frameworkBin Liu, Ren Long, Kuo‐Chen Chou — 2016
• LECTINPred: web Server that Uses Complex Networks of Protein Structure for Prediction of Lectins with Potential Use as Cancer Biomarkers or in Parasite Vaccine DesignIsabel Gómez, Jaume Jorba Bisbal, Àngels Prat — 2014
• PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids compositionYongchun Zuo, Yuan Li, Yingli Chen — 2017
• Web-based drug repurposing tools: a survey桜子 志賀 — 2019
• iRSpot-PDI: Identification of recombination spots by incorporating dinucleotide property diversity information into Chou's pseudo components.Манжуева Оксана Михайловна — 2019
• Discovering meaning from biological sequences: focus on predicting misannotated proteins, binding patterns, and g4-quadruplex secondary structuresVasant Honavar, Drena Dobbs, Carson M. Andorf — 2013
• Predicting eukaryotic protein secretion without signals.Henrik Nielsen, Eirini Petsalaki, Linlin Zhao — 2019

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