| scholarly article | Q13442814 |
| P50 | author | Waqar Hussain | Q100994382 |
| P2093 | author name string | Kuo-Chen Chou | |
| Sher Afzal Khan | |||
| Yaser Daanial Khan | |||
| Adeel Ashraf | |||
| Sarah Ilyas | |||
| P2860 | cites work | 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 | ||
| iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition. | Q50975299 | ||
| iUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model. | Q51034002 | ||
| iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. | Q51068065 | ||
| iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. | Q51261419 | ||
| Prediction and analysis of protein methylarginine and methyllysine based on Multisequence features. | Q51572029 | ||
| Diffusion-controlled effects in reversible enzymatic fast reaction systems - critical spherical shell and proximity rate constant | Q52435811 | ||
| Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou's general pseudo amino acid composition. | Q52563485 | ||
| Post-translational modifications of the insect sulfakinins: sulfation, pyroglutamate-formation and O-methylation of glutamic acid. | Q52571564 | ||
| The critical spherical shell in enzymatic fast reaction systems | Q52751534 | ||
| iRNA-3typeA: Identifying Three Types of Modification at RNA's Adenosine Sites. | Q55162021 | ||
| An extension of Chou's graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways | Q24529160 | ||
| Graphical rules for enzyme-catalysed rate laws | Q24530755 | ||
| WebLogo: A Sequence Logo Generator | Q27860646 | ||
| Regulation of heterochromatin by histone methylation and small RNAs | Q28261891 | ||
| Prediction of protein domain with mRMR feature selection and analysis | Q28480439 | ||
| PMeS: prediction of methylation sites based on enhanced feature encoding scheme | Q28480447 | ||
| 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 | ||
| iCTX-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels | Q28658162 | ||
| iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins | Q28675957 | ||
| CD-HIT: accelerated for clustering the next-generation sequencing data | Q29616664 | ||
| A composite framework for the statistical analysis of epidemiological DNA methylation data with the Infinium Human Methylation 450K BeadChip | Q30816621 | ||
| Computational identification of protein methylation sites through bi-profile Bayes feature extraction | Q33418619 | ||
| 2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function | Q33633983 | ||
| iMethyl-PseAAC: identification of protein methylation sites via a pseudo amino acid composition approach | Q33745509 | ||
| iHyd-PseAAC: predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition | Q33755615 | ||
| The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism | Q34027486 | ||
| PSNO: predicting cysteine S-nitrosylation sites by incorporating various sequence-derived features into the general form of Chou's PseAAC. | Q34072148 | ||
| Graphic rule for drug metabolism systems | Q34113910 | ||
| Low-frequency collective motion in biomacromolecules and its biological functions | Q34163861 | ||
| Applications of graph theory to enzyme kinetics and protein folding kinetics. Steady and non-steady-state systems | Q34207713 | ||
| Steady-state kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-87201E | Q34305893 | ||
| Protein methylation. | Q34337295 | ||
| Graphic rules in steady and non-steady state enzyme kinetics | Q34535225 | ||
| iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition | Q34579675 | ||
| iPTM-mLys: identifying multiple lysine PTM sites and their different types | Q34677601 | ||
| Role of protein methylation in regulation of transcription | Q35915940 | ||
| Reversing histone methylation | Q36240192 | ||
| Pse-Analysis: a python package for DNA/RNA and protein/ peptide sequence analysis based on pseudo components and kernel methods | Q36246564 | ||
| Kinetic studies with the non-nucleoside human immunodeficiency virus type-1 reverse transcriptase inhibitor U-90152E. | Q42275206 | ||
| The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase | Q42278148 | ||
| iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences. | Q42315793 | ||
| iRNA-PseU: Identifying RNA pseudouridine sites | Q42319212 | ||
| Prediction of protein S-nitrosylation sites based on adapted normal distribution bi-profile Bayes and Chou's pseudo amino acid composition | Q42737215 | ||
| Using subsite coupling to predict signal peptides | Q43571662 | ||
| Using ensemble SVM to identify human GPCRs N-linked glycosylation sites based on the general form of Chou's PseAAC. | Q45849318 | ||
| Prediction of signal peptides using scaled window | Q45882553 | ||
| iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. | Q45999913 | ||
| iDNA4mC: identifying DNA N4-methylcytosine sites based on nucleotide chemical properties. | Q46298862 | ||
| pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information | Q47309310 | ||
| pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites | Q47313652 | ||
| iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. | Q47316901 | ||
| iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier | Q47437268 | ||
| iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. | Q47557569 | ||
| A Novel Modeling in Mathematical Biology for Classification of Signal Peptides | Q47559177 | ||
| 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 composition | Q47562851 | ||
| pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC. | Q47657928 | ||
| Prediction of lysine crotonylation sites by incorporating the composition of k-spaced amino acid pairs into Chou's general PseAAC. | Q47698008 | ||
| iRNA-2methyl: Identify RNA 2'-O-methylation Sites by Incorporating Sequence-Coupled Effects into General PseKNC and Ensemble Classifier | Q47791798 | ||
| Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou׳s general PseAAC. | Q47817205 | ||
| iPGK-PseAAC: Identify Lysine Phosphoglycerylation Sites in Proteins by Incorporating Four Different Tiers of Amino Acid Pairwise Coupling Information into the General PseAAC. | Q47839892 | ||
| Increased citrullination of histone H3 in multiple sclerosis brain and animal models of demyelination: a role for tumor necrosis factor-induced peptidylarginine deiminase 4 translocation. | Q48380864 | ||
| Prediction of HIV-1 and HIV-2 proteins by using Chou's pseudo amino acid compositions and different classifiers | Q49205554 | ||
| Structural and quantum mechanical computations to elucidate the altered binding mechanism of metal and drug with pyrazinamidase from Mycobacterium tuberculosis due to mutagenicity. | Q50054209 | ||
| PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework | Q50165014 | ||
| iRNA-Methyl: Identifying N(6)-methyladenosine sites using pseudo nucleotide composition. | Q50567651 | ||
| 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 | ||
| An efficient algorithm for recognition of human actions. | Q55382671 | ||
| Iris recognition using image moments and k-means algorithm. | Q55474387 | ||
| iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC. | Q55513264 | ||
| Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC | Q56976875 | ||
| Using deformation energy to analyze nucleosome positioning in genomes | Q57272901 | ||
| Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC | Q57467559 | ||
| iMethyl-STTNC: Identification of N-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences | Q57467581 | ||
| iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC | Q57476467 | ||
| iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC | Q57480510 | ||
| 1 Protein Methyltransferases: Their Distribution Among the Five Structural Classes of AdoMet-Dependent Methyltransferases | Q57531911 | ||
| Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome | Q57786254 | ||
| Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework | Q57823572 | ||
| Fu-SulfPred: Identification of Protein S-sulfenylation Sites by Fusing Forests via Chou's General PseAAC | Q58575869 | ||
| Bastion3: a two-layer ensemble predictor of type III secreted effectors | Q58615513 | ||
| Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC | Q59353033 | ||
| PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids | Q60301419 | ||
| Positive-unlabelled learning of glycosylation sites in the human proteome | Q62729890 | ||
| iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites | Q62729918 | ||
| Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods | Q62730213 | ||
| Enzymatic methylation of protein fractions from calf thymus nuclei | Q72300291 | ||
| Expression of nitric oxide related enzymes in coronary heart disease | Q83358170 | ||
| Using Chou's general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains | Q87868005 | ||
| iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC | Q88500130 | ||
| Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC | Q88571151 | ||
| pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC | Q88906176 | ||
| pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC | Q90237915 | ||
| iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition | Q90358625 | ||
| pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments | Q90503965 | ||
| pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset | Q90578667 | ||
| pLoc_bal-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by General PseAAC and Quasi-balancing Training Dataset | Q90659198 | ||
| SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins | Q90798893 | ||
| BlaPred: Predicting and classifying β-lactamase using a 3-tier prediction system via Chou's general PseAAC | Q91107197 | ||
| pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset | Q91282652 | ||
| pLoc_bal-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC | Q91367801 | ||
| iRNA(m6A)-PseDNC: Identifying N6-methyladenosine sites using pseudo dinucleotide composition | Q91368318 | ||
| Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC | Q91606497 | ||
| SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins | Q91623056 | ||
| Advance in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs | Q91797925 | ||
| iPhosH-PseAAC: Identify phosphohistidine sites in proteins by blending statistical moments and position relative features according to the Chou's 5-step rule and general pseudo amino acid composition | Q92394011 | ||
| Simulated Protein Thermal Detection (SPTD) for Enzyme Thermostability Study and an Application Example for Pullulanase from Bacillus deramificans | Q93025831 | ||
| pLoc_bal-mPlant: Predict Subcellular Localization of Plant Proteins by General PseAAC and Balancing Training Dataset | Q93205927 | ||
| pNitro-Tyr-PseAAC: Predict Nitrotyrosine Sites in Proteins by Incorporating Five Features into Chou's General PseAAC | Q93385734 | ||
| dbPTM 2016: 10-year anniversary of a resource for post-translational modification of proteins | Q36434875 | ||
| iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition | Q36740900 | ||
| Naïve Bayes classifier with feature selection to identify phage virion proteins | Q36898465 | ||
| iACP: a sequence-based tool for identifying anticancer peptides | Q37086151 | ||
| iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition | Q37376225 | ||
| iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC. | Q37376337 | ||
| Histone methylation and ubiquitination with their cross-talk and roles in gene expression and stability | Q37445597 | ||
| iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC. | Q37536872 | ||
| iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier | Q37588275 | ||
| iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components | Q37645097 | ||
| Histone lysine methylation and demethylation pathways in cancer | Q37800638 | ||
| 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 | ||
| pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. | Q38429700 | ||
| pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. | Q38430978 | ||
| pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC. | Q38431305 | ||
| iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites | Q38501790 | ||
| Recent Progress in Predicting Posttranslational Modification Sites in Proteins | Q38569194 | ||
| A Prediction Model for Membrane Proteins Using Moments Based Features. | Q38576100 | ||
| iPhos-PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory | Q38794654 | ||
| iPreny-PseAAC: identify C-terminal cysteine prenylation sites in proteins by incorporating two tiers of sequence couplings into PseAAC. | Q38829040 | ||
| iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals | Q38973888 | ||
| A Treatise to Computational Approaches Towards Prediction of Membrane Protein and Its Subtypes. | Q39014505 | ||
| An Unprecedented Revolution in Medicinal Chemistry Driven by the Progress of Biological Science | Q39245677 | ||
| iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition | Q39392218 | ||
| iRSpot-EL: identify recombination spots with an ensemble learning approach | Q39484248 | ||
| pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC. | Q39646826 | ||
| 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 | ||
| pRNAm-PC: Predicting N(6)-methyladenosine sites in RNA sequences via physical-chemical properties | Q40989473 | ||
| iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition | Q41093262 | ||
| Prediction of N-linked glycosylation sites using position relative features and statistical moments. | Q41347885 | ||
| Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines | Q41353622 | ||
| iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach | Q41684110 | ||
| iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals | Q41709408 | ||
| Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy | Q41952252 | ||
| iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition | Q41968991 | ||
| iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC. | Q42075633 | ||
| P433 | issue | 4 | |
| P921 | main subject | statistics | Q12483 |
| P304 | page(s) | 275-292 | |
| P577 | publication date | 2019-05-01 | |
| P1433 | published in | Current Genomics | Q5195047 |
| P1476 | title | 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 | |
| P478 | volume | 20 |
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