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[1]
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Deep-piRNA: bBi-layered prediction model for PIWI-Iinteracting RNA using discriminative features
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… , Materials & Continua,
2022 |
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[2]
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Characterization and Prediction of Dengue Virus targeting peptides based on three class of descriptors using k-NN and Random Forest algorithm.
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2022 |
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[3]
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MethEvo: an accurate evolutionary information-based methylation site predictor
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Neural Computing and …,
2022 |
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[4]
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Molecular cloning, expression and in silico analyses of calcium˗ dependent protein kinase 2 (CDPK2) in potato (Solanum tuberosum L.)
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South African Journal of Botany,
2022 |
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[5]
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Matrikines as Mediators of Tissue Remodelling
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Advanced Drug Delivery …,
2022 |
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[6]
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DeepSSPred: A Deep Learning Based Sulfenylation Site Predictor Via a Novel nSegmented Optimize Federated Feature Encoder
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Protein and Peptide Letters,
2021 |
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[7]
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Improving the classification of protein sequence functions by reducing the heterogeneity of datasets
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2021 |
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[8]
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Characterization and Prediction of Dengue Virus Targeting Peptides Based on Combined Amino Acid Composition Descriptors Using Random Forest Algorithm …
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2021 |
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[9]
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iTSP-PseAAC: identifying tumor suppressor proteins by using fully connected neural network and PseAAC
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Current …,
2021 |
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[10]
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iSUMOK-PseAAC: prediction of lysine sumoylation sites using statistical moments and Chou's PseAAC
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PeerJ,
2021 |
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[11]
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iEnhancer-MFGBDT: Identifying enhancers and their strength by fusing multiple features and gradient boosting decision tree
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… Biosciences and Engineering,
2021 |
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[12]
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How can artificial intelligence be used for peptidomics?
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Expert Review of …,
2021 |
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[13]
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ProtoPred: Advancing Oncological Research Through Identification of Proto-Oncogene Proteins
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2021 |
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[14]
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iAtbP-Hyb-EnC: Prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model
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Computers in Biology …,
2021 |
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[15]
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4mC-RF: improving the prediction of 4mC sites using composition and position relative features and statistical moment
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Analytical Biochemistry,
2021 |
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[16]
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Molecular Docking and Dynamics Simulation Analysis of Thymoquinone and Thymol Compounds from Nigella sativa L. that Inhibit Cag A and Vac A Oncoprotein of …
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Medicinal Chemistry,
2021 |
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[17]
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Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre-and early treatment predicts pathologic complete …
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BioMedical …,
2021 |
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[18]
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Prediction of rna 5-hydroxymethylcytosine modifications using deep learning
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2021 |
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[19]
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Characterization and Prediction of Presynaptic and Postsynaptic Neurotoxins Based on Reduced Amino Acids and Biological Properties
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2021 |
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[20]
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Molecular Docking and Dynamics Simulation Analysis of Thymoquinone and Thymol Compounds from Nigella sativa L. that Inhibit Cag A and Vac A Oncoprotein of Helicobacter pylori: Probable Treatment of H. pylori Infections
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Medicinal Chemistry,
2021 |
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[21]
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Sequence-based Identification of Allergen Proteins Developed by Integration of PseAAC and Statistical Moments via 5-Step Rule
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2020 |
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[22]
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Identification of lysine carboxylation sites in proteins by integrating statistical moments and position relative features via general PseAAC
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2020 |
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[23]
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DeepEnzyPred: A Bi-Layered Deep Learning Framework for prediction of Bacteriophage Enzymes and their Sub-Hydrolases Enzymes via Novel Multi Level …
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2020 |
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[24]
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Recent Progresses for Computationally Identifying N 6-methyladenosine Sites in Saccharomyces cerevisiae
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2020 |
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[25]
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An intelligent computational model for prediction of promoters and their strength via natural language processing
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2020 |
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[26]
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A deep learning-based computational approach for discrimination of DNA N6-methyladenosine sites by fusing heterogeneous features
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2020 |
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[27]
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Identifying Enhancers and Their Strength by the Integration of Word Embedding and Convolution Neural Network
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2020 |
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[28]
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iHyd-ProSite: A novel Computational Approach for Identifying Hydroxylation Sites in Proline Via Mathematical Modeling
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2020 |
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[29]
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Plant-mSubP: a computational framework for the prediction of single-and multi-target protein subcellular localization using integrated machine-learning approaches
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2020 |
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[30]
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Locate-R: Subcellular localization of long non-coding RNAs using nucleotide compositions
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2020 |
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[31]
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Identification of Human Secretome and Membrane Proteome-Based Cancer Biomarkers Utilizing Bioinformatics
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2020 |
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[32]
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Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques
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2020 |
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[33]
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iProtease-PseAAC (2L): A two-layer predictor for identifying proteases and their types using Chou's 5-step-rule and general PseAAC
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2020 |
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[34]
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Use of Chou's 5-Steps Rule to Reveal Active Compound and Mechanism of Shuangshen Pingfei San on Idiopathic Pulmonary Fibrosis
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2020 |
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[35]
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Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening
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2020 |
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[36]
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Comparison and Analysis of Computational Methods for Identifying N6-Methyladenosine Sites in Saccharomyces cerevisiae
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2020 |
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[37]
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Using Chou's 5-steps rule to predict O-linked serine glycosylation sites by blending position relative features and statistical moment
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… ACM transactions on …,
2020 |
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[38]
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IPhosS (Deep)-PseAAC: Identify phosphoserine sites in proteins using deep learning on general pseudo amino acid compositions via modified 5-Steps rule
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IEEE/ACM Transactions …,
2020 |
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[39]
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Identification of functional piRNAs using a convolutional neural network
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IEEE/ACM Transactions on …,
2020 |
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[40]
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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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2019 |
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[41]
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Advances in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs
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2019 |
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[42]
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Recent progresses in predicting protein subcellular localization with artificial intelligence (AI) tools developed via the 5‐steps rule
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2019 |
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[43]
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UbiSitePred: A novel method for improving the accuracy of ubiquitination sites prediction by using LASSO to select the optimal Chou's pseudo components
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2019 |
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[44]
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Impacts of pseudo amino acid components and 5-steps rule to proteomics and proteome analysis
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2019 |
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[45]
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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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2019 |
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[46]
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iMethylK-PseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General …
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2019 |
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[47]
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iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou's 5-steps Rule and Pseudo Components
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2019 |
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[48]
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cACP: Classifying anticancer peptides using discriminative intelligent model via Chou's 5-step rules and general pseudo components
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2019 |
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[49]
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HeteroDualNet: A Dual Convolutional Neural Network With Heterogeneous Layers for Drug-Disease Association Prediction via Chou's Five-Step Rule
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2019 |
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[50]
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iPredCNC: Computational prediction model for cancerlectins and non-cancerlectins using novel cascade features subset selection
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2019 |
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[51]
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An energy model for recognizing the prokaryotic promoters based on molecular structure
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2019 |
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[52]
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RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule
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2019 |
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[53]
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Plant-mSubP: a computational framework for the prediction of single and multi-target protein subcellular localization using integrated machine-learning …
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2019 |
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[54]
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RBPro-RF: Use Chou's 5-steps rule to predicting RNA-binding proteins via random forest with elastic net
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2019 |
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[55]
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Recent progresses in predicting protein subcellular localization with artificial intelligence (AI) tools developed via the 5-steps rule
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2019 |
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[56]
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Detecting De Novo Plasmodesmata Targeting Signals and Identifying PD Targeting Proteins
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2019 |
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[57]
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Identification of Phage Virion Proteins by Using the g-gap Tripeptide Composition
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2019 |
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[58]
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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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2019 |
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[59]
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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
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2019 |
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[60]
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Application of Machine Learning Techniques to Predict Protein Phosphorylation Sites
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2019 |
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[61]
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Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs
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2019 |
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[62]
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The preliminary efficacy evaluation of the CTLA-4-Ig treatment against Lupus nephritis through in-silico analyses
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2019 |
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[63]
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iHyd-PseAAC (EPSV): Identifying Hydroxylation Sites in Proteins by Extracting Enhanced Position and Sequence Variant Feature via Chou's 5-Step Rule and General …
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2019 |
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[64]
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Structural Variability in the RLR-MAVS Pathway and Sensitive Detection of Viral RNAs
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2019 |
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[65]
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Structure‐guided identification of antimicrobial peptides in the spathe transcriptome of the non‐model plant, arum lily (Zantedeschia aethiopica)
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2019 |
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[66]
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In silico minimalist approach to study 2D HP protein folding into an inhomogeneous space mimicking osmolyte effect: First trial in the search of foldameric backbones
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2019 |
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[67]
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Established and In-trial GPCR Families in Clinical Trials: A Review for Target Selection
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2019 |
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[68]
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Sequence and structure‐based characterization of ubiquitination sites in human and yeast proteins using Chou's sample formulation
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2019 |
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[69]
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iAFP-gap-SMOTE: An Efficient Feature Extraction Scheme Gapped Dipeptide Composition is Coupled with an Oversampling Technique for Identification of Antifreeze …
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2019 |
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[70]
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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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2019 |
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[71]
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iMotor-CNN: Identifying molecular functions of cytoskeleton motor proteins using 2D convolutional neural network via Chou's 5-step rule
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2019 |
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[72]
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iDHS-DMCAC: identifying DNase I hypersensitive sites with balanced dinucleotide-based detrending moving-average cross-correlation coefficient
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2019 |
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[73]
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An Epidemic Avian Influenza Prediction Model Based on Google Trends
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2019 |
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[74]
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DAKB-GPCRs: An Integrated Computational Platform for Drug Abuse Related GPCRs
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2019 |
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[75]
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Bioimage-based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks
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2019 |
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[76]
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LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion
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2019 |
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[77]
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Plant protection product dose rate estimation in apple orchards using a fuzzy logic system
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2019 |
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[78]
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iSS-CNN: Identifying splicing sites using convolution neural network
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2019 |
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[79]
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Recent Advances in Computational Methods for Identifying Anticancer Peptides
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2019 |
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[80]
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Identification and characterization of WD40 superfamily genes in peach
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2019 |
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[81]
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Prediction of Nitrosocysteine Sites Using Position and Composition Variant Features
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2019 |
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[82]
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Advance in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs
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2019 |
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[83]
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DeepIon: Deep learning approach for classifying ion transporters and ion channels from membrane proteins
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2019 |
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[84]
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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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2019 |
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[85]
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iRSpot-SPI: Deep learning-based recombination spots prediction by incorporating secondary sequence information coupled with physio-chemical properties via …
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2019 |
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[86]
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Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation
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2019 |
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[87]
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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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2019 |
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[88]
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pLoc_bal-mEuk: predict subcellular localization of eukaryotic proteins by general PseAAC and quasi-balancing training dataset
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2019 |
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[89]
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The multiple applications and possible mechanisms of the hyperbaric oxygenation therapy
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2019 |
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[90]
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iHyd-PseAAC (EPSV): Identifying Hydroxylation Sites in Proteins by Extracting Enhanced Position and Sequence Variant Feature via Chou's 5-Step Rule and …
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2019 |
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[91]
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A two-level computation model based on deep learning algorithm for identification of piRNA and their functions via Chou's 5-steps rule
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2019 |
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[92]
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Inhibition of α-amylase Activity by Zn2+: Insights from Spectroscopy and Molecular Dynamics Simulations
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2019 |
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[93]
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Tensor Algebra-based Geometrical (3D) Biomacro-Molecular Descriptors for Protein Research: Theory, Applications and Comparison with other Methods
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2019 |
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[94]
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A classification model for lncRNA and mRNA based on k-mers and a convolutional neural network
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2019 |
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[95]
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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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2019 |
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[96]
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MsDBP: Exploring DNA-binding Proteins by Integrating Multi-scale Sequence Information via Chou's 5-steps Rule
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Journal of Proteome Research,
2019 |
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[97]
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MsDBP: Exploring DNA-Binding Proteins by Integrating Multiscale Sequence Information via Chou's Five-Step Rule
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2019 |
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[98]
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iDHS-DSAMS: Identifying DNase I hypersensitive sites based on the dinucleotide property matrix and ensemble bagged tree
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2019 |
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[99]
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Physicochemical n‐Grams Tool: A tool for protein physicochemical descriptor generation via Chou's 5‐steps rule
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2019 |
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[100]
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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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2019 |
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[101]
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Identifying FL11 subtype by characterizing tumor immune microenvironment in prostate adenocarcinoma via Chou's 5-steps rule
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2019 |
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[102]
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19F-NMR in Target-based Drug Discovery
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2019 |
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[103]
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Identifying N6-methyladenosine sites using extreme gradient boosting system optimized by particle swarm optimizer
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2019 |
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[104]
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Speed up differential evolution for computationally expensive protein structure prediction problems
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2019 |
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[105]
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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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2019 |
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[106]
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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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2019 |
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[107]
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pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments
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2019 |
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[108]
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EPAI-NC: Enhanced prediction of adenosine to inosine RNA editing sites using nucleotide compositions
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2019 |
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[109]
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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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2019 |
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[110]
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HRGPred: Prediction of herbicide resistant genes with k-mer nucleotide compositional features and support vector machine
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2019 |
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[111]
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MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters
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2019 |
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[112]
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iRNA-PseKNC (2methyl): Identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components
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2019 |
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[113]
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iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding
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2019 |
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[114]
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Analysis and prediction of animal toxins by various Chou's pseudo components and reduced amino acid compositions
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2019 |
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[115]
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Hydrogen bond analysis of the EGFR-ErbB3 heterodimer related to non-small cell lung cancer and drug resistance
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2019 |
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[116]
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Predicting protein–protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach
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2019 |
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[117]
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Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou's general PseAAC
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2019 |
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[118]
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On the internal correlations of protein sequences probed by non-alignment methods: Novel signatures for drug and antibody targets via the Burrows-Wheeler …
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2019 |
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[119]
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4mCCNN: Identification of N4-methylcytosine Sites in Prokaryotes Using Convolutional Neural Network
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IEEE Access,
2019 |
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[120]
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Identifying DNase I hypersensitive sites using multi-features fusion and F-score features selection via Chou's 5-steps rule
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2019 |
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[121]
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csDMA: an improved bioinformatics tool for identifying DNA 6 mA modifications via Chou's 5-step rule
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2019 |
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[122]
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Advances in Electrochemistry for Monitoring Cellular Chemical Flux
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2019 |
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[123]
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AtbPpred: A robust sequence-based prediction of anti-tubercular peptides using extremely randomized trees
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2019 |
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[124]
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Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks
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2019 |
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[125]
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The Inhibition of Polysialyltranseferase ST8SiaIV Through Heparin Binding to Polysialyltransferase Domain (PSTD)
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2019 |
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[126]
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Identification of prokaryotic promoters and their strength by integrating heterogeneous features
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2019 |
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[127]
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iPPI-PseAAC (CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC
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Journal of Theoretical Biology,
2019 |
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[128]
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Improved DNA-binding protein identification by incorporating evolutionary information into the Chou's PseAAC
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2018 |
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[129]
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Fu-SulfPred: Identification of Protein S-sulfenylation Sites by Fusing Forests via Chou's General PseAAC
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Journal of Theoretical Biology,
2018 |
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[130]
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piRNN: deep learning algorithm for piRNA prediction
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2018 |
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[131]
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iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC
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Genomics,
2018 |
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[132]
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iRO-3wPseKNC: Identify DNA replication origins by three-window-based PseKNC
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2018 |
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[133]
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BlaPred: Predicting and classifying β-lactamase using a 3-tier prediction system via Chou's general PseAAC
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Journal of Theoretical Biology,
2018 |
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[134]
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iRNA (m6A)-PseDNC: identifying N6-methyladenosine sites using pseudo dinucleotide composition
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Analytical Biochemistry,
2018 |
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[135]
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Analysis and prediction of ion channel inhibitors by using feature selection and Chou's general pseudo amino acid composition
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Journal of Theoretical Biology,
2018 |
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[136]
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pLoc_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC
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Journal of Theoretical Biology,
2018 |
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[137]
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pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset
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Genomics,
2018 |
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[138]
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pDHS-DSET: Prediction of DNase I hypersensitive sites in plant genome using DS evidence theory
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Analytical Biochemistry,
2018 |
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[139]
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pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC
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Genomics,
2018 |
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Study of human dopamine sulfotransferases based on gene expression programming
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Analysis of rate-limiting long-range contacts in the folding rate of three-state and two-state Proteins
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Predicting apoptosis protein subcellular location with PseAAC by incorporating tripeptide composition
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QSAR Study on MHC Class IA Alleles Based on the Novel Parameters of Amino Acids
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Predicting the activity of ACE inhibitory peptides with a novel mode of pseudo amino acid composition
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3D QSAR pharmacophore modeling, in silico screening, and density functional theory (DFT) approaches for identification of human chymase inhibitors
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Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context
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Application of density similarities to predict membrane protein types based on pseudo-amino acid composition
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3D-QSAR study on a series of Bcl-2 protein inhibitors using comparative molecular field analysis
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Using the concept of pseudo amino acid composition to predict resistance gene against< i> Xanthomonas oryzae pv. oryzae in rice: An approach from chaos games representation
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Predicting protein folding rate from amino acid sequence
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Improved prediction of palmitoylation sites using PWMs and SVM
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Identify submitochondria and subchloroplast locations with pseudo amino acid composition: approach from the strategy of discrete wavelet transform feature extraction
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Identification of potent EGFR inhibitors from TCM Database@ Taiwan
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Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition
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Studies on the rules of β-strand alignment in a protein β-sheet structure
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A Novel Method to Predict Protein-Protein Interactions Based on the Information of Protein-Protein Interaction Networks and Protein Sequence
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A theoretical and numerical multiscale framework for the analysis of pattern formation in protein crystal engineering
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Prediction of Michaelis-Menten constant of beta-glucosidases using nitrophenyl-beta-D-glucopyranoside as substrate
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A Novel Fuzzy Fisher Classifier for Signal Peptide Prediction
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Theoretical study of GSK−3α: neural networks QSAR studies for the design of new inhibitors using 2D descriptors
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Discrimination of Thermostable and Thermophilic Lipases using Support Vector Machines
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Evidence Obtained from anova to Reason Cross‐species Infection and Cross‐subtype Mutation in Neuraminidases of Influenza A Viruses
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A novel motif discovery algorithm for identifying protein families
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Identification of functionally diverse lipocalin proteins from sequence information using support vector machine
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A simple method to analyze the similarity of biological sequences based on the fuzzy theory
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Engineering thermostable xylanase enzyme mutant from Bacillus halodurans
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Prediction of disease-related genes based on hybrid features
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Graphic rule for drug metabolism systems
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Fitting Evolution of Matrix Protein 1 from Influenza A Virus Using Analytical Solution of System of Differential Equations
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Prediction of enzyme subfamily class via pseudo amino acid composition by incorporating the conjoint triad feature
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Prediction of subcellular location of apoptosis proteins using pseudo amino acid composition: an approach from auto covariance transformation
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Protein subcellular multi-localization prediction using a min-max modular support vector machine
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International journal of neural systems,
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A summary of computational resources for protein phosphorylation
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A classification-based prediction model of messenger RNA polyadenylation sites
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A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction
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Using the concept of Chou's pseudo amino acid composition to predict enzyme family classes: an approach with support vector machine based on discrete wavelet transform
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Molecular modeling of cytochrome P450 and drug metabolism
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Predicting drug-target interaction networks based on functional groups and biological features
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PloS one,
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Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks
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Prediction of apoptosis protein locations with genetic algorithms and support vector machines through a new mode of pseudo amino acid composition
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Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition
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High performance set of PseAAC and sequence based descriptors for protein classification
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Sequence-based prediction of enzyme thermostability through bioinformatics algorithms
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Prediction of the parallel/antiparallel orientation of beta-strands using amino acid pairing preferences and support vector machines
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Utilizing shared interacting domain patterns and Gene Ontology information to improve protein–protein interaction prediction
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Identifying the hub proteins from complicated membrane protein network systems
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Predicting protein solubility with a hybrid approach by pseudo amino acid composition
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Development of tools and database for analysis of metal binding sites in protein
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Protein and peptide letters ,
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Protein classification using texture descriptors extracted from the protein backbone image
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The Burrows–Wheeler similarity distribution between biological sequences based on Burrows–Wheeler transform
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Predicting caspase substrate cleavage sites based on a hybrid SVMPSSM method
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Gene ontology-based protein function prediction by using sequence composition information
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e-PROPAINOR: A Web-Server for Fast Prediction of Cα Structure & Likely Functional Sites of a Protein Sequence
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Prediction of cyclin proteins using Chou's pseudo amino acid composition
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Protein and Peptide Letters,
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Gpos-mPLoc: A top-down approach to improve the quality of predicting subcellular localization of Gram-positive bacterial proteins
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Identify GPCRs and their types with Chou's pseudo amino acid composition: an approach from multi-scale energy representation and position specific …
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A Multi-Label Predictor for Identifying Eukaryotic Protein Subcellular Locations by Incorporating the Gene Ontology and Dipeptide Composition Information …
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X Wang
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