Predicting protein submitochondria locations by combining different descriptors into the general form of Chou’s pseudo amino acid composition
出版年份 2011 全文链接
标题
Predicting protein submitochondria locations by combining different descriptors into the general form of Chou’s pseudo amino acid composition
作者
关键词
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出版物
AMINO ACIDS
Volume 43, Issue 2, Pages 545-555
出版商
Springer Nature
发表日期
2011-11-19
DOI
10.1007/s00726-011-1143-4
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Using Pseudo Amino Acid Composition to Predict Protease Families by Incorporating a Series of Protein Biological Features
- (2012) Lele Hu et al. PROTEIN AND PEPTIDE LETTERS
- Knowledge-based virtual screening of HLA-A*0201-restricted CD8+ T-cell epitope peptides from herpes simplex virus genome
- (2011) Jianjun Bi et al. JOURNAL OF THEORETICAL BIOLOGY
- iLoc-Virus: A multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites
- (2011) Xuan Xiao et al. JOURNAL OF THEORETICAL BIOLOGY
- Prediction of GABAA receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine
- (2011) Hassan Mohabatkar et al. JOURNAL OF THEORETICAL BIOLOGY
- iLoc-Euk: A Multi-Label Classifier for Predicting the Subcellular Localization of Singleplex and Multiplex Eukaryotic Proteins
- (2011) Kuo-Chen Chou et al. PLoS One
- A Multi-Label Classifier for Predicting the Subcellular Localization of Gram-Negative Bacterial Proteins with Both Single and Multiple Sites
- (2011) Xuan Xiao et al. PLoS One
- Identify Golgi Protein Types with Modified Mahalanobis Discriminant Algorithm and Pseudo Amino Acid Composition
- (2011) Hui Ding et al. PROTEIN AND PEPTIDE LETTERS
- High performance set of PseAAC and sequence based descriptors for protein classification
- (2010) Loris Nanni et al. JOURNAL OF THEORETICAL BIOLOGY
- SecretP: Identifying bacterial secreted proteins by fusing new features into Chou’s pseudo-amino acid composition
- (2010) Lezheng Yu et al. JOURNAL OF THEORETICAL BIOLOGY
- Prediction of protein submitochondria locations based on data fusion of various features of sequences
- (2010) Pooya Zakeri et al. JOURNAL OF THEORETICAL BIOLOGY
- Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition
- (2010) Hao Lin et al. JOURNAL OF THEORETICAL BIOLOGY
- Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition
- (2010) Maqsood Hayat et al. JOURNAL OF THEORETICAL BIOLOGY
- AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties
- (2010) Krishna Kumar Kandaswamy et al. JOURNAL OF THEORETICAL BIOLOGY
- Some remarks on protein attribute prediction and pseudo amino acid composition
- (2010) Kuo-Chen Chou JOURNAL OF THEORETICAL BIOLOGY
- Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization
- (2010) Kuo-Chen Chou et al. PLoS One
- A New Method for Predicting the Subcellular Localization of Eukaryotic Proteins with Both Single and Multiple Sites: Euk-mPLoc 2.0
- (2010) Kuo-Chen Chou et al. PLoS One
- SVMCRYS: An SVM Approach for the Prediction of Protein Crystallization Propensity from Protein Sequence
- (2010) Krishna Kandaswamy et al. PROTEIN AND PEPTIDE LETTERS
- Using the Concept of Chous Pseudo Amino Acid Composition to Predict Enzyme Family Classes: An Approach with Support Vector Machine Based on Discrete Wavelet Transform
- (2010) Jian-Ding Qiu et al. PROTEIN AND PEPTIDE LETTERS
- Characteristic Peptides of Protein Secondary Structural Motifs
- (2010) Rajani R. Joshi et al. PROTEIN AND PEPTIDE LETTERS
- Prediction of G-Protein-Coupled Receptor Classes in Low Homology Using Chous Pseudo Amino Acid Composition with Approximate Entropy and Hydrophobicity Patterns
- (2010) Quan Gu et al. PROTEIN AND PEPTIDE LETTERS
- Prediction of Subcellular Location of Apoptosis Proteins Using Pseudo Amino Acid Composition: An Approach from Auto Covariance Transformation
- (2010) Taigang Liu et al. PROTEIN AND PEPTIDE LETTERS
- Prediction of Cyclin Proteins Using Chous Pseudo Amino Acid Composition
- (2010) Hassan Mohabatkar PROTEIN AND PEPTIDE LETTERS
- PROTEIN SECONDARY STRUCTURE PREDICTION USING NMR CHEMICAL SHIFT DATA
- (2010) YUZHONG ZHAO et al. Journal of Bioinformatics and Computational Biology
- Improving discrimination of outer membrane proteins by fusing different forms of pseudo amino acid composition
- (2009) Qing-Bin Gao et al. ANALYTICAL BIOCHEMISTRY
- Pseudo Amino Acid Composition and its Applications in Bioinformatics, Proteomics and System Biology
- (2009) Kuo-Chen Chou Current Proteomics
- Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach
- (2009) Yu-hong Zeng et al. JOURNAL OF THEORETICAL BIOLOGY
- Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses
- (2009) Maryam Esmaeili et al. JOURNAL OF THEORETICAL BIOLOGY
- Predicting subcellular location of proteins using integrated-algorithm method
- (2009) Yu-Dong Cai et al. MOLECULAR DIVERSITY
- Prediction of Cell Wall Lytic Enzymes Using Chous Amphiphilic Pseudo Amino Acid Composition
- (2009) Hui Ding et al. PROTEIN AND PEPTIDE LETTERS
- Prediction of Protein Secondary Structure Content by Using the Concept of Chous Pseudo Amino Acid Composition and Support Vector Machine
- (2009) Chao Chen et al. PROTEIN AND PEPTIDE LETTERS
- Using Chou’s pseudo amino acid composition based on approximate entropy and an ensemble of AdaBoost classifiers to predict protein subnuclear location
- (2008) Xiaoying Jiang et al. AMINO ACIDS
- Prediction of subcellular location apoptosis proteins with ensemble classifier and feature selection
- (2008) Quan Gu et al. AMINO ACIDS
- Genetic programming for creating Chou’s pseudo amino acid based features for submitochondria localization
- (2008) Loris Nanni et al. AMINO ACIDS
- Improving subcellular localization prediction using text classification and the gene ontology
- (2008) A. Fyshe et al. BIOINFORMATICS
- ProLoc-GO: Utilizing informative Gene Ontology terms for sequence-based prediction of protein subcellular localization
- (2008) Wen-Lin Huang et al. BMC BIOINFORMATICS
- Predicting the cofactors of oxidoreductases based on amino acid composition distribution and Chou's amphiphilic pseudo-amino acid composition
- (2008) Guang-Ya Zhang et al. JOURNAL OF THEORETICAL BIOLOGY
- The modified Mahalanobis Discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition
- (2008) Hao Lin JOURNAL OF THEORETICAL BIOLOGY
- Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition
- (2008) D.N. Georgiou et al. JOURNAL OF THEORETICAL BIOLOGY
- Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms
- (2008) Kuo-Chen Chou et al. Nature Protocols
- Protein networks markedly improve prediction of subcellular localization in multiple eukaryotic species
- (2008) KiYoung Lee et al. NUCLEIC ACIDS RESEARCH
- Predicting Lipase Types by Improved Chous Pseudo-Amino Acid Composition
- (2008) Guang-Ya Zhang et al. PROTEIN AND PEPTIDE LETTERS
- Predicting Subcellular Localization of Mycobacterial Proteins by Using Chous Pseudo Amino Acid Composition
- (2008) Hao Lin et al. PROTEIN AND PEPTIDE LETTERS
- Using the Concept of Chous Pseudo Amino Acid Composition to Predict Apoptosis Proteins Subcellular Location: An Approach by Approximate Entropy
- (2008) Xiaoying Jian et al. PROTEIN AND PEPTIDE LETTERS
- Predicting Protein Subcellular Location Using Chous Pseudo Amino Acid Composition and Improved Hybrid Approach
- (2008) Feng-Min Li et al. PROTEIN AND PEPTIDE LETTERS
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