[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]
|
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]
|
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]
|
Advances in Electrochemistry for Monitoring Cellular Chemical Flux
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2019 |
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[123]
|
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]
|
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]
|
Improved DNA-binding protein identification by incorporating evolutionary information into the Chou's PseAAC
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2018 |
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[129]
|
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]
|
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]
|
iRO-3wPseKNC: Identify DNA replication origins by three-window-based PseKNC
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2018 |
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[133]
|
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]
|
iRNA (m6A)-PseDNC: identifying N6-methyladenosine sites using pseudo dinucleotide composition
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|
Analytical Biochemistry,
2018 |
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[135]
|
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]
|
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]
|
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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[140]
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iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou's Pseudo components
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Genomics,
2018 |
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[141]
|
pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC
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|
Bioinformatics,
2018 |
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[142]
|
Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC
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|
Journal of Theoretical Biology,
2018 |
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[143]
|
iMethyl-STTNC: Identification of N6-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences
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|
Journal of Theoretical Biology,
2018 |
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[144]
|
Implications of newly identified brain eQTL genes and their interactors in Schizophrenia
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|
Molecular Therapy - Nucleic Acids,
2018 |
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[145]
|
In silico analysis of Plasmodium falciparum CDPK5 protein through molecular modeling, docking and dynamics
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|
Journal of Theoretical Biology,
2018 |
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[146]
|
Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC
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|
Molecular Biology Reports,
2018 |
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[147]
|
iPro70-FMWin: identifying Sigma70 promoters using multiple windowing and minimal features
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|
Molecular Genetics and Genomics,
2018 |
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[148]
|
Predicting lysine lipoylation sites using bi-profile bayes feature extraction and fuzzy support vector machine algorithm
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|
Analytical Biochemistry,
2018 |
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[149]
|
Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC
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|
Journal of Theoretical Biology,
2018 |
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[150]
|
Large-scale frequent stem pattern mining in RNA families
|
|
Journal of Theoretical Biology,
2018 |
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[151]
|
Characterize the difference between TMPRSS2-ERG and non-TMPRSS2-ERG fusion patients by clinical and biological characteristics in prostate cancer
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|
Gene,
2018 |
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[152]
|
A Comprehensive In Silico Method to Study the QSTR of the Aconitine Alkaloids for Designing Novel Drugs
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Molecules,
2018 |
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[153]
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A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes
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Scientific Reports,
2018 |
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[154]
|
The Relationship Between DNA Methylation in Key Region and the Differential Expressions of Genes in Human Breast Tumor Tissue
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2018 |
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Constructing a linear QSAR for some metabolizable drugs by human or pig flavin-containing monooxygenases using some molecular features selected by a genetic algorithm trained SVM
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Predicting anticancer peptides with Chou′ s pseudo amino acid composition and investigating their mutagenicity via Ames test
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Virus-ECC-mPLoc: a multi-label predictor for predicting the subcellular localization of virus proteins with both single and multiple sites based on a general form of Chou's pseudo amino acid composition
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Multilabel learning via random label selection for protein subcellular multilocations prediction
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A novel ensemble and composite approach for classifying proteins based on Chou's pseudo amino acid composition
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A simple k-word interval method for phylogenetic analysis of DNA sequences
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Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou's pseudo amino acid compositions
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A hybrid computational model for the effects of maspin on cancer cell dynamics
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Journal of theoretical biology ,
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Use of multivariate chemometric algorithms on 1 H NMR data to assess a soluble fiber ( Plantago ovata husk) nutritional intervention
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Prediction of Methylation Sites Using the Composition of K-Spaced Amino Acid Pairs
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Protein and peptide letters,
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Linear regression model of short k-word: a similarity distance suitable for biological sequences with various lengths
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Predicting the heats of combustion of polynitro arene, polynitro heteroarene, acyclic and cyclic nitramine, nitrate ester and nitroaliphatic compounds using bee algorithm and adaptive neuro-fuzzy inference system
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Metallo-β-lactamases: structural features, antibiotic recognition, inhibition, and inhibitor design
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Prediction of essential proteins in prokaryotes by incorporating various physico-chemical features into the general form of Chou's pseudo amino acid composition
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Protein and peptide letters,
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Analysis of codon use features of stearoyl-acyl carrier protein desaturase gene in Camellia sinensis
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Identification of antioxidants from sequence information using Naive Bayes
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Predicting ion channel-drug interactions based on sequence-derived features and functional groups
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Using the concept of Chou's pseudo amino acid composition to predict protein solubility: An approach with entropies in information theory
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A challenge for medicinal chemistry by the 17β-hydroxysteroid dehydrogenase superfamily: an integrated biological function and inhibition study
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Protein space: a natural method for realizing the nature of protein universe
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Stability of halophilic proteins: from dipeptide attributes to discrimination classifier
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Efficacy of function specific 3D-motifs in enzyme classification according to their EC-numbers
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Unconventional Interaction Forces in Protein and Protein-ligand Systems and their Impacts to Drug Design
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The Prediction of Calpain Cleavage Sites with the mRMR and IFS Approaches
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Discriminating lysosomal membrane protein types using dynamic neural network
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Recent Advances on QSAR-Based Profiling of Agonist and Antagonist A3 Adenosine Receptor Ligands
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Characterizing the Functional Similarity of Enzymes with High Co-Citation in Interaction Networks
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Protein and peptide letters,
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Metagenome Assembly Validation: Which Metagenome Contigs are Bona Fide?
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Investigation Binding Patterns of Human Carboxylesterase I (hCES I) with Broad Substrates by MD Simulations
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TRAINER: A General-Purpose Trainable Short Biosequence Classifer
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Protein and peptide letters,
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Predicting Protein Model Quality from Sequence Alignments by Support Vector Machines
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Prediction of Protein Methylation Sites Using Conditional Random Field
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Engineering and analysis of protease fine specificity via site-directed mutagenesis
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Using Cheminformatics for the Identification of Biological Functions of Small Molecules in Metabolic Pathway
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Target network analysis of adiponectin, a multifaceted adipokine
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New Markov–Shannon Entropy models to assess connectivity quality in complex networks: from molecular to cellular pathway, parasite–host, neural, industry, and legal–social networks
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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 scoring matrix
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Using the improved position specific scoring matrix and ensemble learning method to predict drug-binding residues from protein sequences
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Interrogating noise in protein sequences from the perspective of protein–protein interactions prediction
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A Large-Scale Comparison of Computational Models on the Residue Flexibility for NMR-derived Proteins
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Predicting gram-positive bacterial protein subcellular localization based on localization motifs
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RFCRYS: Sequence-based protein crystallization propensity prediction by means of random forest
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Predicting the metabolic pathways of small molecules based on their physicochemical properties
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Prediction of human major histocompatibility complex class II binding peptides by continuous kernel discrimination method
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SVM prediction of ligand-binding sites in bacterial lipoproteins employing shape and physio-chemical descriptors
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Predicting deleterious non-synonymous single nucleotide polymorphisms in signal peptides based on hybrid sequence attributes
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Predicting the Classification of Transcription Factors by Incorporating their Binding Site Properties into a Novel Mode of Chou's Pseudo Amino Acid Composition
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Machine learning study of Classifiers trained with Biophysiochemical properties of amino acids to predict fibril forming peptide motifs
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Annotating the protein-RNA interaction sites in proteins using evolutionary information and protein backbone structure
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Prediction of nicotinamide adenine dinucleotide interacting sites based on ensemble support vector machine
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A pharmacophore model specific to active site of CYP1A2 with a novel molecular modeling explorer and CoMFA
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Multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization
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Predicting plant protein subcellular multi-localization by Chou's PseAAC formulation based multi-label homolog knowledge transfer learning
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Identifying bacterial virulent proteins by fusing a set of classifiers based on variants of Chou's pseudo amino acid composition and on evolutionary information
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Jointly handling potency and toxicity of antimicrobial peptidomimetics by simple rules from desirability theory and chemoinformatics
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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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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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Engineering thermostable xylanase enzyme mutant from Bacillus halodurans
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Computational prediction of properties and analysis of molecular phylogenetics of polyketide synthases in three species of Actinomycetes
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Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition
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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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