| scholarly article | Q13442814 |
| P8978 | DBLP publication ID | journals/bioinformatics/QiuSXXC16 |
| P356 | DOI | 10.1093/BIOINFORMATICS/BTW380 |
| P698 | PubMed publication ID | 27334473 |
| P50 | author | Kuo-Chen Chou | Q30069869 |
| Xuan Xiao | Q78691395 | ||
| P2093 | author name string | Wang-Ren Qiu | |
| Zhao-Chun Xu | |||
| Bi-Qian Sun | |||
| P2860 | cites work | Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival Forests | Q22673963 |
| iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins | Q28235305 | ||
| Recent progress in protein subcellular location prediction | Q28240670 | ||
| iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition | Q28486031 | ||
| iNitro-Tyr: prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition | Q28541975 | ||
| iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins | Q28675957 | ||
| Prediction of protein structure classes by incorporating different protein descriptors into general Chou's pseudo amino acid composition. | Q30364744 | ||
| RSARF: prediction of residue solvent accessibility from protein sequence using random forest method | Q30407107 | ||
| PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions | Q30459140 | ||
| Computational identification of protein methylation sites through bi-profile Bayes feature extraction | Q33418619 | ||
| iHyd-PseAAC: predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition | Q33755615 | ||
| Prediction of protein cellular attributes using pseudo-amino acid composition | Q33941503 | ||
| iDNA-Prot: identification of DNA binding proteins using random forest with grey model | Q34026228 | ||
| Subcellular location prediction of apoptosis proteins | Q34163319 | ||
| PseAAC-Builder: a cross-platform stand-alone program for generating various special Chou's pseudo-amino acid compositions | Q34264398 | ||
| Prediction of Protein Structural Classes | Q34297604 | ||
| propy: a tool to generate various modes of Chou's PseAAC. | Q34328788 | ||
| Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes | Q34340793 | ||
| Prediction of human immunodeficiency virus protease cleavage sites in proteins | Q34395418 | ||
| Chou's pseudo amino acid composition improves sequence-based antifreeze protein prediction | Q34415164 | ||
| Discrimination of acidic and alkaline enzyme using Chou's pseudo amino acid composition in conjunction with probabilistic neural network model | Q34450984 | ||
| Prediction of β-lactamase and its class by Chou's pseudo-amino acid composition and support vector machine | Q34451095 | ||
| An intriguing controversy over protein structural class prediction | Q34492544 | ||
| iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition | Q34579675 | ||
| MeMo: a web tool for prediction of protein methylation modifications. | Q34974385 | ||
| repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects | Q35518468 | ||
| Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences | Q35810290 | ||
| iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition | Q36740900 | ||
| iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier | Q37588275 | ||
| PseAAC-General: fast building various modes of general form of Chou's pseudo-amino acid composition for large-scale protein datasets | Q37683961 | ||
| Some remarks on protein attribute prediction and pseudo amino acid composition | Q37822128 | ||
| Some remarks on predicting multi-label attributes in molecular biosystems | Q38093788 | ||
| Impacts of bioinformatics to medicinal chemistry | Q38303748 | ||
| Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳s general PseAAC. | Q38472058 | ||
| iLoc-Gpos: a multi-layer classifier for predicting the subcellular localization of singleplex and multiplex Gram-positive bacterial proteins | Q38500720 | ||
| iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites | Q38501790 | ||
| Virus-PLoc: a fusion classifier for predicting the subcellular localization of viral proteins within host and virus-infected cells | Q38517309 | ||
| Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences | Q38534551 | ||
| Recent Progress in Predicting Posttranslational Modification Sites in Proteins | Q38569194 | ||
| iPhos-PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory | Q38794654 | ||
| pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC. | Q39646826 | ||
| iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework | Q39788351 | ||
| iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset | Q40152361 | ||
| iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition | Q40420289 | ||
| Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition | Q40534208 | ||
| iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples | Q40593974 | ||
| Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC. | Q40670313 | ||
| pRNAm-PC: Predicting N(6)-methyladenosine sites in RNA sequences via physical-chemical properties | Q40989473 | ||
| MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou's pseudo amino acid composition and a novel multi-label classifier | Q41030889 | ||
| iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach | Q41489664 | ||
| Molecular science for drug development and biomedicine | Q42583554 | ||
| Prediction of membrane protein types by incorporating amphipathic effects | Q45068778 | ||
| Signal-3L: A 3-layer approach for predicting signal peptides | Q45871359 | ||
| Prediction of signal peptides using scaled window | Q45882553 | ||
| A multilabel model based on Chou's pseudo-amino acid composition for identifying membrane proteins with both single and multiple functional types. | Q45929953 | ||
| PseDNA-Pro: DNA-Binding Protein Identification by Combining Chou's PseAAC and Physicochemical Distance Transformation. | Q45955470 | ||
| Perspectives in Medicinal Chemistry. | Q45956756 | ||
| iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. | Q45999913 | ||
| Editorial: current progress in structural bioinformatics of protein-biomolecule interactions | Q46556559 | ||
| Identification of immunoglobulins using Chou's pseudo amino acid composition with feature selection technique. | Q50711525 | ||
| pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. | Q50727441 | ||
| iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets. | Q50729299 | ||
| iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins. | Q50761949 | ||
| iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC. | Q50926107 | ||
| Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. | Q51025519 | ||
| iUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model. | Q51034002 | ||
| PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. | Q51094090 | ||
| iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. | Q51261419 | ||
| iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites. | Q51523634 | ||
| AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. | Q51639977 | ||
| P433 | issue | 20 | |
| P407 | language of work or name | English | Q1860 |
| P304 | page(s) | 3116-3123 | |
| P577 | publication date | 2016-06-22 | |
| P1433 | published in | Bioinformatics | Q4914910 |
| P1476 | title | iPTM-mLys: identifying multiple lysine PTM sites and their different types | |
| P478 | volume | 32 |
| Q42378453 | 2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications |
| Q33633983 | 2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function |
| Q42362058 | A novel feature ranking method for prediction of cancer stages using proteomics data. |
| Q64892461 | Analysis and prediction of human acetylation using a cascade classifier based on support vector machine. |
| Q47216858 | Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate. |
| Q49403730 | DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest |
| Q55442966 | Detecting Succinylation sites from protein sequences using ensemble support vector machine. |
| Q46420725 | Evolutionary mechanism and biological functions of 8-mers containing CG dinucleotide in yeast |
| Q37526122 | GPS-PAIL: prediction of lysine acetyltransferase-specific modification sites from protein sequences. |
| Q46237755 | Gene Prediction in Metagenomic Fragments with Deep Learning |
| Q37690994 | Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human |
| Q92442964 | Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks |
| Q92190975 | Identifying Acetylation Protein by Fusing Its PseAAC and Functional Domain Annotation |
| Q50062451 | Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams. |
| Q60926316 | LAIPT: Lysine Acetylation Site Identification with Polynomial Tree |
| Q57025160 | Large-scale comparative assessment of computational predictors for lysine post-translational modification sites |
| Q45944202 | MLACP: machine-learning-based prediction of anticancer peptides. |
| Q33677773 | Molecular classification of prostate adenocarcinoma by the integrated somatic mutation profiles and molecular network |
| Q45947594 | Multi-label classifier based on histogram of gradients for predicting the anatomical therapeutic chemical class/classes of a given compound. |
| Q30838333 | Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC. |
| Q92600598 | Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou's 5-steps rule and general pseudo components |
| Q39452127 | Prediction of nucleosome positioning by the incorporation of frequencies and distributions of three different nucleotide segment lengths into a general pseudo k-tuple nucleotide composition |
| Q42040786 | Prediction of post-translational modification sites using multiple kernel support vector machine |
| Q33918369 | Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou's pseudo components |
| Q41472472 | Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression |
| Q36246564 | Pse-Analysis: a python package for DNA/RNA and protein/ peptide sequence analysis based on pseudo components and kernel methods |
| Q58696200 | PseUI: Pseudouridine sites identification based on RNA sequence information |
| Q91699024 | RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule |
| Q90347783 | RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites |
| Q110950379 | SNARE-CNN: a 2D convolutional neural network architecture to identify SNARE proteins from high-throughput sequencing data |
| Q58746202 | Sc-ncDNAPred: A Sequence-Based Predictor for Identifying Non-coding DNA in |
| Q42254397 | Sequence-based predictive modeling to identify cancerlectins |
| Q90659183 | Structural Variability in the RLR-MAVS Pathway and Sensitive Detection of Viral RNAs |
| Q91708649 | Two-Level Protein Methylation Prediction using structure model-based features |
| Q90069170 | csDMA: an improved bioinformatics tool for identifying DNA 6 mA modifications via Chou's 5-step rule |
| Q41709408 | iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals |
| Q38973888 | iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals |
| Q47557569 | iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. |
| Q47385944 | iDNAProt-ES: Identification of DNA-binding Proteins Using Evolutionary and Structural Features |
| Q90289562 | iHyd-PseAAC (EPSV): Identifying Hydroxylation Sites in Proteins by Extracting Enhanced Position and Sequence Variant Feature via Chou's 5-Step Rule and General Pseudo Amino Acid Composition |
| Q47437268 | iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier |
| Q89584735 | iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC via Chou's 5-steps Rule |
| Q47953719 | iMulti-HumPhos: a multi-label classifier for identifying human phosphorylated proteins using multiple kernel learning based support vector machines. |
| Q91704420 | iNR-2L: A two-level sequence-based predictor developed via Chou's 5-steps rule and general PseAAC for identifying nuclear receptors and their families |
| Q39392218 | iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition |
| Q90358625 | iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition |
| Q57476467 | iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC |
| Q47316901 | iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. |
| Q57928510 | iPromoter-FSEn: Identification of bacterial σ promoter sequences using feature subspace based ensemble classifier |
| Q42315793 | iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences. |
| Q42075633 | iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC. |
| Q41093262 | iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition |
| Q90359474 | iRSpot-DTS: Predict recombination spots by incorporating the dinucleotide-based spare-cross covariance information into Chou's pseudo components |
| Q39484248 | iRSpot-EL: identify recombination spots with an ensemble learning approach |
| Q52651537 | iRSpot-PDI: Identification of recombination spots by incorporating dinucleotide property diversity information into Chou's pseudo components. |
| Q57929342 | iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou's Pseudo components |
| Q89584745 | iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou's 5-steps Rule and Pseudo Components |
| Q49592504 | ir-HSP: Improved Recognition of Heat Shock Proteins, Their Families and Sub-types Based On g-Spaced Di-peptide Features and Support Vector Machine |
| Q52623895 | pDHS-ELM: computational predictor for plant DNase I hypersensitive sites based on extreme learning machines. |
| Q47313652 | pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites |
| Q38429700 | pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. |
| Q47657928 | pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC. |
| Q47309310 | pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information |
| Q38431305 | pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC. |
| Q88906176 | pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC |
| Q91282652 | pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset |
| Q39646826 | pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC. |
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