Natural Science

Natural Science

ISSN Print: 2150-4091
ISSN Online: 2150-4105
www.scirp.org/journal/ns
E-mail: ns@scirp.org
"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):
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[7] Using Chou's 5-Step Rule to Predict DNA-Protein Binding with Multi-scale Complementary Feature
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[11] A comprehensive review of the imbalance classification of protein post-translational modifications
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[12] Application of machine learning for drug–target interaction prediction
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[13] Structural and functional changes of catalase through interaction with Erlotinib hydrochloride. Use of Chou's 5-steps rule to study mechanisms
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[14] Some Opinions or Comments on the Five Important Papers Published Recently
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[15] Robust ensemble of handcrafted and learned approaches for DNA-binding proteins
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[16] iSUMOK-PseAAC: prediction of lysine sumoylation sites using statistical moments and Chou's PseAAC
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[17] iORI-ENST: identifying origin of replication sites based on elastic net and stacking learning
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[18] Identify RNA-associated subcellular localizations based on multi-label learning using Chou's 5-steps rule
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[19] The significant and profound impacts of Gordon life science institute
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[20] Predicting lncRNA subcellular localization using unbalanced pseudo-k nucleotide compositions
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[21] Investigating Deep Feedforward Neural Networks for Classification of Transposon-Derived piRNAs
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[22] 2Gram Features Based Prediction of Membrane Protein Types Using Ensemble Classifiers Methods
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[23] iPseU-Layer: Identifying RNA Pseudouridine Sites Using Layered Ensemble Model.
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[24] Mathfeature: feature extraction package for biological sequences based on mathematical descriptors
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[25] Recent progresses for computationally identifying N6-methyladenosine sites in Saccharomyces cerevisiae
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[26] Using the Chou's 5-steps rule to predict splice junctions with interpretable bidirectional long short-term memory networks
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[27] Distorted key theory and its implication for drug development
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[28] cACP: Classifying anticancer peptides using discriminative intelligent model via Chou's 5-step rules and general pseudo components
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[29] Feature Selection and Classification for Gene Expression Data Using Novel Correlation Based Overlapping Score Method via Chou's 5-Steps Rule
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[30] Some illuminating remarks on molecular genetics and genomics as well as drug development
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[31] Development of a new oligonucleotide block location-based feature extraction (BLBFE) method for the classification of riboswitches
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[32] Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening
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[33] Classification of riboswitch families using block location-based feature extraction (BLBFE) method
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[34] EnhancerP-2L: A Gene regulatory site identification tool for DNA enhancer region using CREs motifs
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[35] RBPro-RF: Use Chou's 5-steps rule to predict RNA-binding proteins via random forest with elastic net
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[36] 6mA-RicePred: A method for identifying DNA N6-methyladenine sites in the rice genome based on feature fusion
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[37] iterb-PPse: Identification of transcriptional terminators in bacterial by incorporating nucleotide properties into PseKNC
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[38] The Development of Gordon Life Science Institute: Its Driving Force and Accomplishments
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[39] pLoc_Deep-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by Deep Learning
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[41] pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning
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[42] Use Chou's 5-steps rule to predict remote homology proteins by merging grey incidence analysis and domain similarity analysis
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[43] Prediction of antioxidant proteins using hybrid feature representation method and random forest
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[44] Gordon Life Science Institute and Its Impacts on Computational Biology and Drug Development
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[45] Using Evolutionary Information and Multi-Label Linear Discriminant Analysis to Predict the Subcellular Location of Multi-Site Bacterial Proteins via Chou's 5-Steps …
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[46] Use Chou's 5-steps rule to identify DNase I hypersensitive sites via dinucleotide property matrix and extreme gradient boosting
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[47] EnACP: An Ensemble Learning Model for Identification of Anticancer Peptides
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[48] An intelligent computational model for prediction of promoters and their strength via natural language processing
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[49] Using Chou's Five-steps Rule to Classify and Predict Glutathione S-transferases with Different Machine Learning Algorithms and Pseudo Amino Acid Composition
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[50] Using Chou's 5-steps rule to identify N6-methyladenine sites by ensemble learning combined with multiple feature extraction methods
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[51] iRNA-m5C_NB: A Novel Predictor to Identify RNA 5-Methylcytosine Sites Based on the Naive Bayes Classifier
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[52] N-GlycoGo: Predicting Protein N-Glycosylation Sites on Imbalanced Data Sets by Using Heterogeneous and Comprehensive Strategy
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[53] ML-RBF: Predict protein subcellular locations in a multi-label system using evolutionary features
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[54] Machine Learning Classifiers based on Predicting Membrane Protein using Decision Tree and Random Forest
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[55] Recent Progresses for Computationally Identifying N 6-methyladenosine Sites in Saccharomyces cerevisiae
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[56] Investigating Feedforward Neural Networks for Classification of Transposon-Derived piRNAs
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[57] Identification of ligand-binding residues using protein sequence profile alignment and query-specific support vector machine model
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[58] Characterization of the relationship between FLI1 and immune infiltrate level in tumour immune microenvironment for breast cancer
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[59] Prediction of N6-methyladenosine sites using convolution neural network model based on distributed feature representations
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[60] Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach
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[61] Prediction of Therapeutic Peptides Using Machine Learning: Computational Models, Datasets, and Feature Encodings
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[62] Using the Chou's 5-steps rule, transient overexpression technique, subcellular location, and bioinformatic analysis to verify the function of Vitis vinifera O …
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[63] iGlu_AdaBoost: Identification of Lysine Glutarylation Using the AdaBoost Classifier
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[64] lncLocPred: Predicting LncRNA Subcellular Localization Using Multiple Sequence Feature Information
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[67] QUATgo: Protein quaternary structural attributes predicted by two-stage machine learning approaches with heterogeneous feature encoding
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[68] 2lpiRNApred: a two-layered integrated algorithm for identifying piRNAs and their functions based on LFE-GM feature selection
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[69] Enhancing Segmentation Approaches from Oaam to Fuzzy KC-Means
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[70] Prediction of piRNAs and their function based on discriminative intelligent model using hybrid features into Chou's PseKNC
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[71] Application of Software Engineering Principles to Synthetic Biology and Emerging Regulatory Concerns
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[72] Prediction of m5C Modifications in RNA Sequences by Combining Multiple Sequence Features
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[73] Spark-Based Parallel Deep Neural Network Model for Classification of Large Scale RNAs into piRNAs and Non-piRNAs
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[74] iPseU-Layer: Identifying RNA Pseudouridine Sites Using Layered Ensemble Model
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[75] iPromoter-BnCNN: a novel branched CNN-based predictor for identifying and classifying sigma promoters
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[79] pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset
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[80] Progresses in predicting post-translational modification
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[81] iN6-Methyl (5-step): Identifying RNA N6-methyladenosine sites using deep learning mode via Chou's 5-step rules and Chou's general PseKNC
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[82] iPhosH-PseAAC: Identify phosphohistidine sites in proteins by blending statistical moments and position relative features according to the Chou's 5-step rule and …
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[83] MsDBP: Exploring DNA-binding Proteins by Integrating Multi-scale Sequence Information via Chou's 5-steps Rule
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[84] MsDBP: Exploring DNA-Binding Proteins by Integrating Multiscale Sequence Information via Chou's Five-Step Rule
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[85] iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou's 5-steps Rule and Pseudo Components
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[86] LipoFNT: Lipoylation Sites Identification with Flexible Neural Tree
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[87] LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion
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[88] Physicochemical n‐Grams Tool: A tool for protein physicochemical descriptor generation via Chou's 5‐step rule
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[89] Established and In-trial GPCR Families in Clinical Trials: A Review for Target Selection
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[90] Sequence and structure‐based characterization of ubiquitination sites in human and yeast proteins using Chou's sample formulation
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[91] An Improved Process for Generating Uniform PSSMs and Its Application in Protein Subcellular Localization via Various Global Dimension Reduction Techniques
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[92] iDHS-DMCAC: identifying DNase I hypersensitive sites with balanced dinucleotide-based detrending moving-average cross-correlation coefficient
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[93] RNA Secondary Structure Prediction with Pseudoknots using Chemical Reaction Optimization Algorithm
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[94] An Epidemic Avian Influenza Prediction Model Based on Google Trends
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[95] MFSC: Multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components
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[96] Multidimensional scaling method for prediction of lysine glycation sites
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[97] pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments
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[98] EPAI-NC: Enhanced prediction of adenosine to inosine RNA editing sites using nucleotide compositions
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[99] SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins
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[100] Antigenic: An improved prediction model of protective antigens
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[101] MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters
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[102] iRNA-PseKNC (2methyl): Identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components
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[103] iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding
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[104] Identification of S-nitrosylation sites based on multiple features combination
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[105] SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins
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[106] Bioimage-based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks
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[107] csDMA: an improved bioinformatics tool for identifying DNA 6 mA modifications via Chou's 5-step rule
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[108] Metabolism of Oxalate in Humans: A Potential Role Kynurenine Aminotransferase/Glutamine Transaminase/Cysteine Conjugate Betalyase Plays in Hyperoxaluria
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[109] Identification and characterization of WD40 superfamily genes in peach
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[110] Using Chou's General Pseudo Amino Acid Composition to Classify Laccases from Bacterial and Fungal Sources via Chou's Five-Step Rule
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[113] DeepIon: Deep learning approach for classifying ion transporters and ion channels from membrane proteins
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[120] Artificial intelligence (AI) tools constructed via the 5-steps rule for predicting post-translational modifications
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[121] Impacts of pseudo amino acid components and 5-steps rule to proteomics and proteome analysis
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[122] Recent progresses in predicting protein subcellular localization with artificial intelligence (AI) tools developed via the 5-steps rule
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[123] Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature Extraction via the Chou's 5-steps Rule and General Pseudo Components
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[124] JOURNAL OF MATHEMATICS, STATISTICS AND COMPUTING
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[125] DeepDBP: Deep Neural Networks for Identification of DNA-binding Proteins
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[126] Identifying DNase I hypersensitive sites using multi-features fusion and F-score features selection via Chou's 5-steps rule
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[127] FKRR-MVSF: A Fuzzy Kernel Ridge Regression Model for Identifying DNA-Binding Proteins by Multi-View Sequence Features via Chou's Five-Step Rule
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[128] Rational design, conformational analysis and membrane-penetrating dynamics study of Bac2A-derived antimicrobial peptides against gram-positive clinical strains …
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[129] iN6-methylat (5-step): identifying DNA N6-methyladenine sites in rice genome using continuous bag of nucleobases via Chou's 5-step rule
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[130] MCP: a Multi-Component learning machine to Predict protein secondary structure
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[131] Quantitative Structure-activity Relationship of Acetylcholinesterase Inhibitors based on mRMR Combined with Support Vector Regression
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[133] The multiple applications and possible mechanisms of the hyperbaric oxygenation therapy
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[134] iMotor-CNN: Identifying molecular functions of cytoskeleton motor proteins using 2D convolutional neural network via Chou's 5-step rule
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[135] Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks
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[136] A Review on the Recent Developments of Sequence-based Protein Feature Extraction Methods
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[147] pLoc_bal-mPlant: Predict Subcellular Localization of Plant Proteins by General PseAAC and Balancing Training Dataset
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[148] iPSW (2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition
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[149] iRSpot-DTS: Predict recombination spots by incorporating the dinucleotide-based spare-cross covariance information into Chou's pseudo components
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[150] pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC
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[151] pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC
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[152] iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier
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[153] iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites
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[154] iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC
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[155] Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC
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[156] iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou's Pseudo components
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[157] Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC
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[158] iMethyl-STTNC: Identification of N6-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences
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[159] Using Chou's general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains
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[160] Prediction of HIV-1 and HIV-2 proteins by using Chou's pseudo amino acid compositions and different classifiers
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[161] 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 …
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[162] 4mCPred: Machine learning methods for DNA n4-methylcytosine sites prediction
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[163] BlaPred: Predicting and classifying β-lactamase using a 3-tier prediction system via Chou's general PseAAC
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[164] EvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features
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[165] iPromoter-FSEn: Identification of bacterial σ70 promoter sequences using feature subspace based ensemble classifier
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[166] O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling …
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[167] Sequence clustering in bioinformatics: an empirical study
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[168] Analysis and prediction of ion channel inhibitors by using feature selection and Chou's general pseudo amino acid composition
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[169] Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC
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[170] Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou's general pseudo amino acid composition
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[171] pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset
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[172] pDHS-DSET: Prediction of DNase I hypersensitive sites in plant genome using DS evidence theory
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[173] DPP-PseAAC: A DNA-binding protein prediction model using Chou's general PseAAC
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[174] The Relationship Between DNA Methylation in Key Region and the Differential Expressions of Genes in Human Breast Tumor Tissue
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[175] Predicting Structural Classes of Proteins by Incorporating their Global and Local Physicochemical and Conformational Properties into General Chou's PseAAC
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[176] Improved DNA-binding protein identification by incorporating evolutionary information into the Chou's PseAAC
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[177] iRSpot-ADPM: Identify recombination spots by incorporating the associated dinucleotide product model into Chou's pseudo components
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[178] Unveiling the Transient Protein-Protein Interactions that Regulate the Activity of Human Lemur Tyrosine Kinase-3 (LMTK3) Domain by Cyclin Dependent Kinase 5 …
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[179] Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou's general PseAAC via Kullback-Leibler divergence
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[180] Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC
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[181] Characterization of proteins in different subcellular localizations for Escherichia coli K12
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[182] Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile …
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[183] mLysPTMpred: Multiple Lysine PTM Site Prediction Using Combination of SVM with Resolving Data Imbalance Issue
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[184] Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm
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[185] Sequence-based analysis and prediction of lantibiotics: a machine learning approach
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[186] iRSpot-PDI: Identification of recombination spots by incorporating dinucleotide property diversity information into Chou's pseudo components
Genomics, 2018
[187] Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou's general PseAAC
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[188] Identification of Bacterial Sigma 70 Promoter Sequences Using Feature Subspace Based Ensemble Classifier
2018
[189] Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC
Molecular Biology Reports, 2018
[190] Effective DNA binding protein prediction by using key features via Chou's general PseAAC
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[191] iRecSpot-EF: Effective sequence based features for recombination hotspot prediction
Computers in Biology and Medicine, 2018
[192] NucPosPred: Predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC
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[193] iPro70-FMWin: identifying Sigma70 promoters using multiple windowing and minimal features
Molecular Genetics and Genomics, 2018
[194] iPPI-PseAAC (CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC
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[195] Recognition of the long range enhancer-promoter interactions by further adding DNA structure properties and transcription factor binding motifs in human cell lines
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[196] Predicting lysine lipoylation sites using bi-profile bayes feature extraction and fuzzy support vector machine algorithm
Analytical Biochemistry, 2018
[197] Identification of DNA-Binding Proteins via a Voting Strategy
Current Proteomics, 2018
[198] Fu-SulfPred: Identification of Protein S-sulfenylation Sites by Fusing Forests via Chou's General PseAAC
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[199] DeepEfflux: a 2D convolutional neural network model for identifying families of efflux proteins in transporters
Bioinformatics, 2018
[200] Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition
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[201] Prediction of DNase I hypersensitive sites in plant genome using multiple modes of pseudo components
Analytical Biochemistry, 2018
[202] Predicting protein-protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach
Journal of Theoretical Biology, 2018
[203] Set of approaches based on 3D structure and Position Specific Scoring Matrix for predicting DNA-binding proteins
Bioinformatics, 2018
[204] Analysis and prediction of animal toxins by various Chou's pseudo components and reduced amino acid compositions
Journal of Theoretical Biology, 2018
[205] 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
[206] pNitro-Tyr-PseAAC: Predict Nitrotyrosine Sites in Proteins by Incorporating Five Features into Chou's General PseAAC
2018
[207] Novel Feature Extraction for Predicting Gram-Positive and Gram-Negative Bacteria Protein Sub-cellular Localization
2017
[208] Prediction of therapeutic peptides by incorporating q-Wiener index into Chou's general PseAAC
2017
[209] Predicting membrane protein types using various decision tree classifiers based on various modes of general PseAAC for imbalanced datasets
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[210] iPHLoc-ES: Identification of bacteriophage protein locations using evolutionary and structural features
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[211] Computational prediction of therapeutic peptides based on graph index
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[212] Highly accurate prediction of protein self-interactions by incorporating the average block and PSSM information into the general PseAAC
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[213] Chlorella vulgaris Induces Apoptosis of Human Non-Small Cell Lung Carcinoma (NSCLC) Cells
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[214] Prediction of lysine crotonylation sites by incorporating the composition of k-spaced amino acid pairs into Chou's general PseAAC
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[215] Bi-PSSM: Position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins
Journal of Theoretical Biology, 2017
[216] Idnaprot-es: Identification of DNA-binding proteins using evolutionary and structural features
Scientific Reports, 2017
[217] 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
[218] 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
[219] POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles
Bioinformatics, 2017
[220] MLACP: machine-learning-based prediction of anticancer peptides
Oncotarget, 2017
[221] Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou's pseudo components
Scientific Reports, 2017
[222] BioSeq-Analysis: A platform for DNA, RNA and protein sequence analysis based on machine learning approaches
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[223] 2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications
Oncotarget, 2017
[224] pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites
Bioinformatics, 2017
[225] iRNA-2methyl: Identify RNA 2'-O-methylation Sites by Incorporating Sequence-Coupled Effects into General PseKNC and Ensemble Classifier
Medicinal Chemistry, 2017
[226] iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC
Bioinformatics, 2017
[227] pLoc-mGpos: incorporate key gene ontology information into general PseAAC for predicting subcellular localization of Gram-positive bacterial proteins
2017
[228] pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC
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[229] pLoc-mVirus: predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC
Gene, 2017
[230] An unprecedented revolution in medicinal chemistry driven by the progress of biological science
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[231] iPGK-PseAAC: identify lysine phosphoglycerylation sites in proteins by incorporating four different tiers of amino acid pairwise coupling information into the general …
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[232] New Hot Paper Addresses Machine Learning for Biological Sequences
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