"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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[2] iPseU-Layer: Identifying RNA Pseudouridine Sites Using Layered Ensemble Model
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[3] Spark-Based Parallel Deep Neural Network Model for Classification of Large Scale RNAs into piRNAs and Non-piRNAs
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[4] Prediction of m5C Modifications in RNA Sequences by Combining Multiple Sequence Features
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[5] Application of Software Engineering Principles to Synthetic Biology and Emerging Regulatory Concerns
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[6] Prediction of piRNAs and their function based on discriminative intelligent model using hybrid features into Chou's PseKNC
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[7] Enhancing Segmentation Approaches from Oaam to Fuzzy KC-Means
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[8] 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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[9] Prediction of Therapeutic Peptides Using Machine Learning: Computational Models, Datasets, and Feature Encodings
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[10] Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach
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[11] Prediction of N6-methyladenosine sites using convolution neural network model based on distributed feature representations
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[12] Characterization of the relationship between FLI1 and immune infiltrate level in tumour immune microenvironment for breast cancer
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[13] Identification of ligand-binding residues using protein sequence profile alignment and query-specific support vector machine model
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[14] Investigating Feedforward Neural Networks for Classification of Transposon-Derived piRNAs
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[15] Machine Learning Classifiers based on Predicting Membrane Protein using Decision Tree and Random Forest
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[16] ML-RBF: Predict protein subcellular locations in a multi-label system using evolutionary features
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[17] N-GlycoGo: Predicting Protein N-Glycosylation Sites on Imbalanced Data Sets by Using Heterogeneous and Comprehensive Strategy
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[18] iRNA-m5C_NB: A Novel Predictor to Identify RNA 5-Methylcytosine Sites Based on the Naive Bayes Classifier
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[19] Using Chou's 5-steps rule to identify N6-methyladenine sites by ensemble learning combined with multiple feature extraction methods
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[20] 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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[21] An intelligent computational model for prediction of promoters and their strength via natural language processing
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[22] EnACP: An Ensemble Learning Model for Identification of Anticancer Peptides
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[23] Use Chou's 5-steps rule to identify DNase I hypersensitive sites via dinucleotide property matrix and extreme gradient boosting
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[24] 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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[25] Gordon Life Science Institute and Its Impacts on Computational Biology and Drug Development
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[26] Prediction of antioxidant proteins using hybrid feature representation method and random forest
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[27] Use Chou's 5-steps rule to predict remote homology proteins by merging grey incidence analysis and domain similarity analysis
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[28] pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning
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[29] Use of Chou's 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene …
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[30] pLoc_Deep-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by Deep Learning
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[31] The Development of Gordon Life Science Institute: Its Driving Force and Accomplishments
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[32] 2lpiRNApred: a two-layered integrated algorithm for identifying piRNAs and their functions based on LFE-GM feature selection
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[33] QUATgo: Protein quaternary structural attributes predicted by two-stage machine learning approaches with heterogeneous feature encoding
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[34] Machine learning model to predict oncologic outcomes for drugs in randomized clinical trials
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[35] KD-KLNMF: Identification of lncRNAs subcellular localization with multiple features and nonnegative matrix factorization
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[36] lncLocPred: Predicting LncRNA Subcellular Localization Using Multiple Sequence Feature Information
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[37] iGlu_AdaBoost: Identification of Lysine Glutarylation Using the AdaBoost Classifier
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[38] Distorted key theory and its implication for drug development
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[39] Using the Chou's 5-steps rule to predict splice junctions with interpretable bidirectional long short-term memory networks
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[40] Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening
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[41] Development of a new oligonucleotide block location-based feature extraction (BLBFE) method for the classification of riboswitches
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[42] Some illuminating remarks on molecular genetics and genomics as well as drug development
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[43] RBPro-RF: Use Chou's 5-steps rule to predict RNA-binding proteins via random forest with elastic net
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[44] EnhancerP-2L: A Gene regulatory site identification tool for DNA enhancer region using CREs motifs
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[45] 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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[46] cACP: Classifying anticancer peptides using discriminative intelligent model via Chou's 5-step rules and general pseudo components
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[47] iterb-PPse: Identification of transcriptional terminators in bacterial by incorporating nucleotide properties into PseKNC
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[48] 6mA-RicePred: A method for identifying DNA N6-methyladenine sites in the rice genome based on feature fusion
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[49] iPromoter-BnCNN: a Novel Branched CNN Based Predictor for Identifying and Classifying Sigma Promoters
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[50] Intriguing Story about the Birth of Gordon Life Science Institute and its Development and Driving Force
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[51] DeepDBP: Deep Neural Networks for Identification of DNA-binding Proteins
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[52] JOURNAL OF MATHEMATICS, STATISTICS AND COMPUTING
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[53] 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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[54] Recent progresses in predicting protein subcellular localization with artificial intelligence (AI) tools developed via the 5-steps rule
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[55] Impacts of pseudo amino acid components and 5-steps rule to proteomics and proteome analysis
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[56] Artificial intelligence (AI) tools constructed via the 5-steps rule for predicting post-translational modifications
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[57] CanLect-Pred: A Cancer Therapeutics Tool for Prediction of Target Cancerlectins Using Experiential Annotated Proteomic Sequences
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[58] Applying Machine Learning Algorithms for the Analysis of Biological Sequences and Medical Records
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[59] Recent Advances in Ginsenosides as Potential Therapeutics Against Breast Cancer
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[60] An insightful 20-year recollection since the birth of pseudo amino acid components
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[61] An insightful 10-year recollection since the emergence of the 5-steps rule.
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[62] Gordon life science institute: its philosophy, achievements, and perspective
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[63] Biological Production of (S)-acetoin: A State-of-the-Art Review
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[64] Recent progresses in predicting protein subcellular localization with artificial intelligence (AI) tools developed via the 5‐steps rule
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[65] Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou's 5-steps rule and general pseudo components
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[66] Prediction of aptamer–protein interacting pairs based on sparse autoencoder feature extraction and an ensemble classifier
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[67] iRSpot-SPI: Deep learning-based recombination spots prediction by incorporating secondary sequence information coupled with physio-chemical properties via …
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[68] dForml (KNN)-PseAAC: Detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and pseudo components
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[69] DeepIon: Deep learning approach for classifying ion transporters and ion channels from membrane proteins
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[70] 19F-NMR in Target-based Drug Discovery
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[71] Identifying FL11 subtype by characterizing tumor immune microenvironment in prostate adenocarcinoma via Chou's 5-steps rule
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[72] 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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[73] Identification and characterization of WD40 superfamily genes in peach
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[74] Metabolism of Oxalate in Humans: A Potential Role Kynurenine Aminotransferase/Glutamine Transaminase/Cysteine Conjugate Betalyase Plays in Hyperoxaluria
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[75] csDMA: an improved bioinformatics tool for identifying DNA 6 mA modifications via Chou's 5-step rule
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[76] Bioimage-based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks
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[77] A Review on the Recent Developments of Sequence-based Protein Feature Extraction Methods
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[78] Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks
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[79] iMotor-CNN: Identifying molecular functions of cytoskeleton motor proteins using 2D convolutional neural network via Chou's 5-step rule
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[80] The multiple applications and possible mechanisms of the hyperbaric oxygenation therapy
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[81] Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses
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[82] Quantitative Structure-activity Relationship of Acetylcholinesterase Inhibitors based on mRMR Combined with Support Vector Regression
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[83] MCP: a Multi-Component learning machine to Predict protein secondary structure
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[84] 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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[85] Rational design, conformational analysis and membrane-penetrating dynamics study of Bac2A-derived antimicrobial peptides against gram-positive clinical strains …
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[86] 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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[87] Identifying DNase I hypersensitive sites using multi-features fusion and F-score features selection via Chou's 5-steps rule
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[88] An Epidemic Avian Influenza Prediction Model Based on Google Trends
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[89] RNA Secondary Structure Prediction with Pseudoknots using Chemical Reaction Optimization Algorithm
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[90] iDHS-DMCAC: identifying DNase I hypersensitive sites with balanced dinucleotide-based detrending moving-average cross-correlation coefficient
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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] Sequence and structure‐based characterization of ubiquitination sites in human and yeast proteins using Chou's sample formulation
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[93] Established and In-trial GPCR Families in Clinical Trials: A Review for Target Selection
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[94] Physicochemical n‐Grams Tool: A tool for protein physicochemical descriptor generation via Chou's 5‐step rule
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[95] LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion
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[96] LipoFNT: Lipoylation Sites Identification with Flexible Neural Tree
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[97] iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou's 5-steps Rule and Pseudo Components
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[98] MsDBP: Exploring DNA-Binding Proteins by Integrating Multiscale Sequence Information via Chou's Five-Step Rule
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[99] MsDBP: Exploring DNA-binding Proteins by Integrating Multi-scale Sequence Information via Chou's 5-steps Rule
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[100] 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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[101] 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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[102] Progresses in predicting post-translational modification
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[103] 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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[104] PPI‐Detect: A support vector machine model for sequence‐based prediction of protein–protein interactions
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[105] ELM-MHC: An improved MHC Identification method with Extreme Learning Machine Algorithm
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[106] 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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[107] Identification of S-nitrosylation sites based on multiple features combination
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[108] iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding
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[109] iRNA-PseKNC (2methyl): Identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components
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[110] MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters
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[111] Antigenic: An improved prediction model of protective antigens
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[112] 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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[113] EPAI-NC: Enhanced prediction of adenosine to inosine RNA editing sites using nucleotide compositions
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[114] pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments
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[115] Multidimensional scaling method for prediction of lysine glycation sites
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[116] 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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[117] pNitro-Tyr-PseAAC: Predict Nitrotyrosine Sites in Proteins by Incorporating Five Features into Chou's General PseAAC
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[118] Identification of DNA-Binding Proteins via a Voting Strategy
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[119] Predicting lysine lipoylation sites using bi-profile bayes feature extraction and fuzzy support vector machine algorithm
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[120] 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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[121] iPPI-PseAAC (CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC
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[122] iPro70-FMWin: identifying Sigma70 promoters using multiple windowing and minimal features
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[123] NucPosPred: Predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC
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[124] iRecSpot-EF: Effective sequence based features for recombination hotspot prediction
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[125] Effective DNA binding protein prediction by using key features via Chou's general PseAAC
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[126] Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC
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[127] Identification of Bacterial Sigma 70 Promoter Sequences Using Feature Subspace Based Ensemble Classifier
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[128] Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou's general PseAAC
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[129] iRSpot-PDI: Identification of recombination spots by incorporating dinucleotide property diversity information into Chou's pseudo components
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[130] Sequence-based analysis and prediction of lantibiotics: a machine learning approach
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[131] Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm
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[132] mLysPTMpred: Multiple Lysine PTM Site Prediction Using Combination of SVM with Resolving Data Imbalance Issue
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[133] Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile …
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[134] Characterization of proteins in different subcellular localizations for Escherichia coli K12
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[135] Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC
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[136] 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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[137] 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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[138] Prediction of DNase I hypersensitive sites in plant genome using multiple modes of pseudo components
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[139] 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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[140] DeepEfflux: a 2D convolutional neural network model for identifying families of efflux proteins in transporters
Bioinformatics, 2018
[141] Fu-SulfPred: Identification of Protein S-sulfenylation Sites by Fusing Forests via Chou's General PseAAC
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[142] iRSpot-ADPM: Identify recombination spots by incorporating the associated dinucleotide product model into Chou's pseudo components
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[143] Improved DNA-binding protein identification by incorporating evolutionary information into the Chou's PseAAC
2018
[144] Predicting Structural Classes of Proteins by Incorporating their Global and Local Physicochemical and Conformational Properties into General Chou's PseAAC
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[145] iRSpot-DTS: Predict recombination spots by incorporating the dinucleotide-based spare-cross covariance information into Chou's pseudo components
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[146] 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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[147] pLoc_bal-mPlant: Predict Subcellular Localization of Plant Proteins by General PseAAC and Balancing Training Dataset
2018
[148] Simulated protein thermal detection (SPTD) for enzyme thermostability study and an application example for pullulanase from Bacillus deramificans
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[149] Set of approaches based on 3D structure and position specific-scoring matrix for predicting DNA-binding proteins
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[150] EvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features
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[151] BlaPred: Predicting and classifying β-lactamase using a 3-tier prediction system via Chou's general PseAAC
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[152] 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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[153] Prediction of HIV-1 and HIV-2 proteins by using Chou's pseudo amino acid compositions and different classifiers
Scientific Reports, 2018
[154] 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
[155] The Relationship Between DNA Methylation in Key Region and the Differential Expressions of Genes in Human Breast Tumor Tissue
2018
[156] DPP-PseAAC: A DNA-binding protein prediction model using Chou's general PseAAC
Journal of Theoretical Biology, 2018
[157] pDHS-DSET: Prediction of DNase I hypersensitive sites in plant genome using DS evidence theory
Analytical Biochemistry, 2018
[158] pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset
Genomics, 2018
[159] Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou's general pseudo amino acid composition
Gene, 2018
[160] Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC
Genomics, 2018
[161] Analysis and prediction of ion channel inhibitors by using feature selection and Chou's general pseudo amino acid composition
Journal of Theoretical Biology, 2018
[162] Sequence clustering in bioinformatics: an empirical study
Briefings in Bioinformatics, 2018
[163] O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling …
Bioinformatics, 2018
[164] iPromoter-FSEn: Identification of bacterial σ70 promoter sequences using feature subspace based ensemble classifier
Genomics, 2018
[165] 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
[166] Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC
Journal of Theoretical Biology, 2018
[167] iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou's Pseudo components
Genomics, 2018
[168] Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC
Journal of Theoretical Biology, 2018
[169] 4mCPred: Machine learning methods for DNA n4-methylcytosine sites prediction
Bioinformatics, 2018
[170] iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites
Briefings in Bioinformatics, 2018
[171] iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC
Genomics, 2018
[172] iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier
Genomics, 2018
[173] pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC
Genomics, 2018
[174] pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC
Genomics, 2018
[175] 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
[176] Analysis and prediction of animal toxins by various Chou's pseudo components and reduced amino acid compositions
Journal of Theoretical Biology, 2018
[177] Set of approaches based on 3D structure and Position Specific Scoring Matrix for predicting DNA-binding proteins
Bioinformatics, 2018
[178] Predicting protein-protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach
Journal of Theoretical Biology, 2018
[179] Prediction of therapeutic peptides by incorporating q-Wiener index into Chou's general PseAAC
2017
[180] 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
[181] An unprecedented revolution in medicinal chemistry driven by the progress of biological science
Current Topics in Medicinal Chemistry, 2017
[182] pLoc-mVirus: predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC
Gene, 2017
[183] pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC
Molecular BioSystems, 2017
[184] pLoc-mGpos: incorporate key gene ontology information into general PseAAC for predicting subcellular localization of Gram-positive bacterial proteins
2017
[185] iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC
Bioinformatics, 2017
[186] iRNA-2methyl: Identify RNA 2'-O-methylation Sites by Incorporating Sequence-Coupled Effects into General PseKNC and Ensemble Classifier
Medicinal Chemistry, 2017
[187] pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites
Bioinformatics, 2017
[188] POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles
Bioinformatics, 2017
[189] 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
[190] Idnaprot-es: Identification of DNA-binding proteins using evolutionary and structural features
Scientific Reports, 2017
[191] 2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications
Oncotarget, 2017
[192] BioSeq-Analysis: A platform for DNA, RNA and protein sequence analysis based on machine learning approaches
Briefings in Bioinformatics, 2017
[193] Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou's pseudo components
Scientific Reports, 2017
[194] MLACP: machine-learning-based prediction of anticancer peptides
Oncotarget, 2017
[195] 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
[196] Chlorella vulgaris Induces Apoptosis of Human Non-Small Cell Lung Carcinoma (NSCLC) Cells
Medicinal Chemistry, 2017
[197] Highly accurate prediction of protein self-interactions by incorporating the average block and PSSM information into the general PseAAC
Journal of Theoretical Biology, 2017
[198] 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
[199] Bi-PSSM: Position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins
Journal of Theoretical Biology, 2017
[200] New Hot Paper Addresses Machine Learning for Biological Sequences
2017
[201] Computational prediction of therapeutic peptides based on graph index
Journal of Biomedical Informatics, 2017
[202] iPHLoc-ES: Identification of bacteriophage protein locations using evolutionary and structural features
Journal of Theoretical Biology, 2017
[203] Novel Feature Extraction for Predicting Gram-Positive and Gram-Negative Bacteria Protein Sub-cellular Localization
2017
[204] Predicting membrane protein types using various decision tree classifiers based on various modes of general PseAAC for imbalanced datasets
Journal of Theoretical Biology, 2017
[205] Author Statement
1920
[206] Recent Progresses for Computationally Identifying N 6-methyladenosine Sites in Saccharomyces cerevisiae
[207] Thesis