"Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences"
written by Bin Liu, Hao Wu, Kuo-Chen Chou,
published by Natural Science, Vol.9 No.4, 2017
has been cited by the following article(s):
  • Google Scholar
  • CrossRef
[1] Classification of riboswitch families using block location-based feature extraction (BLBFE) method
Advanced Pharmaceutical Bulletin, 2020
[2] Identifying DNase I hypersensitive sites using multi-features fusion and F-score features selection via Chou's 5-steps rule
2019
[3] FKRR-MVSF: A Fuzzy Kernel Ridge Regression Model for Identifying DNA-Binding Proteins by Multi-View Sequence Features via Chou's Five-Step Rule
2019
[4] Rational design, conformational analysis and membrane-penetrating dynamics study of Bac2A-derived antimicrobial peptides against gram-positive clinical strains …
2019
[5] iN6-methylat (5-step): identifying DNA N6-methyladenine sites in rice genome using continuous bag of nucleobases via Chou's 5-step rule
2019
[6] MCP: a Multi-Component learning machine to Predict protein secondary structure
2019
[7] Quantitative Structure-activity Relationship of Acetylcholinesterase Inhibitors based on mRMR Combined with Support Vector Regression
2019
[8] Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses
2019
[9] The multiple applications and possible mechanisms of the hyperbaric oxygenation therapy
2019
[10] iMotor-CNN: Identifying molecular functions of cytoskeleton motor proteins using 2D convolutional neural network via Chou's 5-step rule
2019
[11] Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks
2019
[12] A Review on the Recent Developments of Sequence-based Protein Feature Extraction Methods
2019
[13] Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou's 5-steps rule and general pseudo components
2019
[14] ELM-MHC: An improved MHC Identification method with Extreme Learning Machine Algorithm
2019
[15] PPI‐Detect: A support vector machine model for sequence‐based prediction of protein–protein interactions
2019
[16] pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset
2019
[17] Progresses in predicting post-translational modification
2019
[18] iN6-Methyl (5-step): Identifying RNA N6-methyladenosine sites using deep learning mode via Chou's 5-step rules and Chou's general PseKNC
2019
[19] iPhosH-PseAAC: Identify phosphohistidine sites in proteins by blending statistical moments and position relative features according to the Chou's 5-step rule and …
2019
[20] MsDBP: Exploring DNA-binding Proteins by Integrating Multi-scale Sequence Information via Chou's 5-steps Rule
Journal of Proteome Research, 2019
[21] MsDBP: Exploring DNA-Binding Proteins by Integrating Multiscale Sequence Information via Chou's Five-Step Rule
2019
[22] iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou's 5-steps Rule and Pseudo Components
2019
[23] LipoFNT: Lipoylation Sites Identification with Flexible Neural Tree
2019
[24] LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion
2019
[25] Physicochemical n‐Grams Tool: A tool for protein physicochemical descriptor generation via Chou's 5‐step rule
2019
[26] Established and In-trial GPCR Families in Clinical Trials: A Review for Target Selection
2019
[27] Sequence and structure‐based characterization of ubiquitination sites in human and yeast proteins using Chou's sample formulation
2019
[28] An Improved Process for Generating Uniform PSSMs and Its Application in Protein Subcellular Localization via Various Global Dimension Reduction Techniques
2019
[29] iDHS-DMCAC: identifying DNase I hypersensitive sites with balanced dinucleotide-based detrending moving-average cross-correlation coefficient
2019
[30] RNA Secondary Structure Prediction with Pseudoknots using Chemical Reaction Optimization Algorithm
2019
[31] An Epidemic Avian Influenza Prediction Model Based on Google Trends
2019
[32] MFSC: Multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components
2019
[33] Multidimensional scaling method for prediction of lysine glycation sites
2019
[34] pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments
2019
[35] EPAI-NC: Enhanced prediction of adenosine to inosine RNA editing sites using nucleotide compositions
2019
[36] SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins
2019
[37] Antigenic: An improved prediction model of protective antigens
2019
[38] MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters
2019
[39] iRNA-PseKNC (2methyl): Identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components
2019
[40] iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding
2019
[41] Identification of S-nitrosylation sites based on multiple features combination
2019
[42] SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins
2019
[43] Bioimage-based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks
2019
[44] csDMA: an improved bioinformatics tool for identifying DNA 6 mA modifications via Chou's 5-step rule
2019
[45] Metabolism of Oxalate in Humans: A Potential Role Kynurenine Aminotransferase/Glutamine Transaminase/Cysteine Conjugate Betalyase Plays in Hyperoxaluria
2019
[46] Identification and characterization of WD40 superfamily genes in peach
2019
[47] Using Chou's General Pseudo Amino Acid Composition to Classify Laccases from Bacterial and Fungal Sources via Chou's Five-Step Rule
2019
[48] Identifying FL11 subtype by characterizing tumor immune microenvironment in prostate adenocarcinoma via Chou's 5-steps rule
2019
[49] 19F-NMR in Target-based Drug Discovery
2019
[50] DeepIon: Deep learning approach for classifying ion transporters and ion channels from membrane proteins
2019
[51] dForml (KNN)-PseAAC: Detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and pseudo components
2019
[52] iRSpot-SPI: Deep learning-based recombination spots prediction by incorporating secondary sequence information coupled with physio-chemical properties via …
2019
[53] Prediction of aptamer–protein interacting pairs based on sparse autoencoder feature extraction and an ensemble classifier
2019
[54] Simulated protein thermal detection (SPTD) for enzyme thermostability study and an application example for pullulanase from Bacillus deramificans
2018
[55] pLoc_bal-mPlant: Predict Subcellular Localization of Plant Proteins by General PseAAC and Balancing Training Dataset
2018
[56] iPSW (2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition
2018
[57] iRSpot-DTS: Predict recombination spots by incorporating the dinucleotide-based spare-cross covariance information into Chou's pseudo components
2018
[58] Set of approaches based on 3D structure and position specific-scoring matrix for predicting DNA-binding proteins
2018
[59] pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC
Genomics, 2018
[60] pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC
Genomics, 2018
[61] iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier
Genomics, 2018
[62] iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites
Briefings in Bioinformatics, 2018
[63] iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC
Genomics, 2018
[64] Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC
Journal of Theoretical Biology, 2018
[65] iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou's Pseudo components
Genomics, 2018
[66] Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC
Journal of Theoretical Biology, 2018
[67] iMethyl-STTNC: Identification of N6-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences
Journal of Theoretical Biology, 2018
[68] Using Chou's general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains
Journal of Theoretical Biology, 2018
[69] Prediction of HIV-1 and HIV-2 proteins by using Chou's pseudo amino acid compositions and different classifiers
Scientific Reports, 2018
[70] iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou's pseudo amino acid …
Journal of Theoretical Biology, 2018
[71] 4mCPred: Machine learning methods for DNA n4-methylcytosine sites prediction
Bioinformatics, 2018
[72] BlaPred: Predicting and classifying β-lactamase using a 3-tier prediction system via Chou's general PseAAC
Journal of Theoretical Biology, 2018
[73] EvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features
Journal of Theoretical Biology, 2018
[74] iPromoter-FSEn: Identification of bacterial σ70 promoter sequences using feature subspace based ensemble classifier
Genomics, 2018
[75] O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling …
Bioinformatics, 2018
[76] Sequence clustering in bioinformatics: an empirical study
Briefings in Bioinformatics, 2018
[77] Analysis and prediction of ion channel inhibitors by using feature selection and Chou's general pseudo amino acid composition
Journal of Theoretical Biology, 2018
[78] Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC
Genomics, 2018
[79] Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou's general pseudo amino acid composition
Gene, 2018
[80] pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset
Genomics, 2018
[81] pDHS-DSET: Prediction of DNase I hypersensitive sites in plant genome using DS evidence theory
Analytical Biochemistry, 2018
[82] DPP-PseAAC: A DNA-binding protein prediction model using Chou's general PseAAC
Journal of Theoretical Biology, 2018
[83] The Relationship Between DNA Methylation in Key Region and the Differential Expressions of Genes in Human Breast Tumor Tissue
2018
[84] Predicting Structural Classes of Proteins by Incorporating their Global and Local Physicochemical and Conformational Properties into General Chou's PseAAC
Journal of Theoretical Biology, 2018
[85] Improved DNA-binding protein identification by incorporating evolutionary information into the Chou's PseAAC
2018
[86] iRSpot-ADPM: Identify recombination spots by incorporating the associated dinucleotide product model into Chou's pseudo components
Journal of Theoretical Biology, 2018
[87] Unveiling the Transient Protein-Protein Interactions that Regulate the Activity of Human Lemur Tyrosine Kinase-3 (LMTK3) Domain by Cyclin Dependent Kinase 5 …
Current Proteomics, 2018
[88] Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou's general PseAAC via Kullback-Leibler divergence
Journal of Theoretical Biology, 2018
[89] Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC
Journal of Theoretical Biology, 2018
[90] Characterization of proteins in different subcellular localizations for Escherichia coli K12
Genomics, 2018
[91] Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile …
PLOS ONE, 2018
[92] mLysPTMpred: Multiple Lysine PTM Site Prediction Using Combination of SVM with Resolving Data Imbalance Issue
2018
[93] Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm
Journal of Theoretical Biology, 2018
[94] Sequence-based analysis and prediction of lantibiotics: a machine learning approach
Computational Biology and Chemistry, 2018
[95] iRSpot-PDI: Identification of recombination spots by incorporating dinucleotide property diversity information into Chou's pseudo components
Genomics, 2018
[96] Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou's general PseAAC
Journal of Theoretical Biology, 2018
[97] Identification of Bacterial Sigma 70 Promoter Sequences Using Feature Subspace Based Ensemble Classifier
2018
[98] Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC
Molecular Biology Reports, 2018
[99] Effective DNA binding protein prediction by using key features via Chou's general PseAAC
Journal of Theoretical Biology, 2018
[100] iRecSpot-EF: Effective sequence based features for recombination hotspot prediction
Computers in Biology and Medicine, 2018
[101] NucPosPred: Predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC
Journal of Theoretical Biology, 2018
[102] iPro70-FMWin: identifying Sigma70 promoters using multiple windowing and minimal features
Molecular Genetics and Genomics, 2018
[103] iPPI-PseAAC (CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC
Journal of Theoretical Biology, 2018
[104] Recognition of the long range enhancer-promoter interactions by further adding DNA structure properties and transcription factor binding motifs in human cell lines
Journal of Theoretical Biology, 2018
[105] Predicting lysine lipoylation sites using bi-profile bayes feature extraction and fuzzy support vector machine algorithm
Analytical Biochemistry, 2018
[106] Identification of DNA-Binding Proteins via a Voting Strategy
Current Proteomics, 2018
[107] Fu-SulfPred: Identification of Protein S-sulfenylation Sites by Fusing Forests via Chou's General PseAAC
Journal of Theoretical Biology, 2018
[108] DeepEfflux: a 2D convolutional neural network model for identifying families of efflux proteins in transporters
Bioinformatics, 2018
[109] Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition
Journal of Theoretical Biology, 2018
[110] Prediction of DNase I hypersensitive sites in plant genome using multiple modes of pseudo components
Analytical Biochemistry, 2018
[111] Predicting protein-protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach
Journal of Theoretical Biology, 2018
[112] Set of approaches based on 3D structure and Position Specific Scoring Matrix for predicting DNA-binding proteins
Bioinformatics, 2018
[113] Analysis and prediction of animal toxins by various Chou's pseudo components and reduced amino acid compositions
Journal of Theoretical Biology, 2018
[114] UbiSitePred: A novel method for improving the accuracy of ubiquitination sites prediction by using LASSO to select the optimal Chou's pseudo components
Chemometrics and Intelligent Laboratory Systems, 2018
[115] pNitro-Tyr-PseAAC: Predict Nitrotyrosine Sites in Proteins by Incorporating Five Features into Chou's General PseAAC
2018
[116] Novel Feature Extraction for Predicting Gram-Positive and Gram-Negative Bacteria Protein Sub-cellular Localization
2017
[117] Prediction of therapeutic peptides by incorporating q-Wiener index into Chou's general PseAAC
2017
[118] Predicting membrane protein types using various decision tree classifiers based on various modes of general PseAAC for imbalanced datasets
Journal of Theoretical Biology, 2017
[119] iPHLoc-ES: Identification of bacteriophage protein locations using evolutionary and structural features
Journal of Theoretical Biology, 2017
[120] Computational prediction of therapeutic peptides based on graph index
Journal of Biomedical Informatics, 2017
[121] Highly accurate prediction of protein self-interactions by incorporating the average block and PSSM information into the general PseAAC
Journal of Theoretical Biology, 2017
[122] Chlorella vulgaris Induces Apoptosis of Human Non-Small Cell Lung Carcinoma (NSCLC) Cells
Medicinal Chemistry, 2017
[123] Prediction of lysine crotonylation sites by incorporating the composition of k-spaced amino acid pairs into Chou's general PseAAC
Journal of Molecular Graphics and Modelling, 2017
[124] Bi-PSSM: Position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins
Journal of Theoretical Biology, 2017
[125] Idnaprot-es: Identification of DNA-binding proteins using evolutionary and structural features
Scientific Reports, 2017
[126] Predict protein structural class by incorporating two different modes of evolutionary information into Chou's general pseudo amino acid composition
Journal of Molecular Graphics and Modelling, 2017
[127] Prediction of lysine propionylation sites using biased svm and incorporating four different sequence features into chou's pseaac
Journal of Molecular Graphics and Modelling, 2017
[128] POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles
Bioinformatics, 2017
[129] MLACP: machine-learning-based prediction of anticancer peptides
Oncotarget, 2017
[130] Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou's pseudo components
Scientific Reports, 2017
[131] BioSeq-Analysis: A platform for DNA, RNA and protein sequence analysis based on machine learning approaches
Briefings in Bioinformatics, 2017
[132] 2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications
Oncotarget, 2017
[133] pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites
Bioinformatics, 2017
[134] iRNA-2methyl: Identify RNA 2'-O-methylation Sites by Incorporating Sequence-Coupled Effects into General PseKNC and Ensemble Classifier
Medicinal Chemistry, 2017
[135] iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC
Bioinformatics, 2017
[136] pLoc-mGpos: incorporate key gene ontology information into general PseAAC for predicting subcellular localization of Gram-positive bacterial proteins
2017
[137] pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC
Molecular BioSystems, 2017
[138] pLoc-mVirus: predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC
Gene, 2017
[139] An unprecedented revolution in medicinal chemistry driven by the progress of biological science
Current Topics in Medicinal Chemistry, 2017
[140] iPGK-PseAAC: identify lysine phosphoglycerylation sites in proteins by incorporating four different tiers of amino acid pairwise coupling information into the general …
Medicinal Chemistry, 2017
[141] New Hot Paper Addresses Machine Learning for Biological Sequences
2017