Kuo-Chen Chou
Orcid: 0000-0001-8857-7063
According to our database1,
Kuo-Chen Chou
authored at least 62 papers
between 2002 and 2021.
Collaborative distances:
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Bibliography
2021
iPhosH-PseAAC: Identify Phosphohistidine Sites in Proteins by Blending Statistical Moments and Position Relative Features According to the Chou's 5-Step Rule and General Pseudo Amino Acid Composition.
IEEE ACM Trans. Comput. Biol. Bioinform., 2021
Using CHOU'S 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment.
IEEE ACM Trans. Comput. Biol. Bioinform., 2021
2020
iLearn : an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data.
Briefings Bioinform., 2020
2019
BMC Bioinform., 2019
MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters.
Bioinform., 2019
Bioinform., 2019
pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC.
Bioinform., 2019
Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework.
Briefings Bioinform., 2019
iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites.
Briefings Bioinform., 2019
Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods.
Briefings Bioinform., 2019
Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.
Briefings Bioinform., 2019
2018
Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors.
Bioinform., 2018
iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC.
Bioinform., 2018
PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy.
Bioinform., 2018
iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC.
Bioinform., 2018
Bioinform., 2018
iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach.
Bioinform., 2018
Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.
Bioinform., 2018
pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information.
Bioinform., 2018
iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences.
Bioinform., 2018
2017
POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles.
Bioinform., 2017
Bioinform., 2017
iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals.
Bioinform., 2017
pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites.
Bioinform., 2017
2016
Bioinform., 2016
iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework.
Bioinform., 2016
pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC.
Bioinform., 2016
iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo <i>k</i>-tuple nucleotide composition.
Bioinform., 2016
2015
Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences.
Nucleic Acids Res., 2015
repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects.
Bioinform., 2015
PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions.
Bioinform., 2015
2014
Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection.
Bioinform., 2014
iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition.
Bioinform., 2014
2011
Proceedings of the 3rd International Conference on Awareness Science and Technology, 2011
2010
J. Comput. Chem., 2010
The computational model to predict accurately inhibitory activity for inhibitors towardsCYP3A4.
Comput. Biol. Medicine, 2010
Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition.
BMC Bioinform., 2010
2009
GPCR-CA: A cellular automaton image approach for predicting G-protein-coupled receptor functional classes.
J. Comput. Chem., 2009
Fragment-based quantitative structure-activity relationship (FB-QSAR) for fragment-based drug design.
J. Comput. Chem., 2009
2008
Comparative Study of Topological Indices of Macro/Supramolecular RNA Complex Networks.
J. Chem. Inf. Model., 2008
Using grey dynamic modeling and pseudo amino acid composition to predict protein structural classes.
J. Comput. Chem., 2008
Multiple field three dimensional quantitative structure-activity relationship (MF-3D-QSAR).
J. Comput. Chem., 2008
2007
Peptide reagent design based on physical and chemical properties of amino acid residues.
J. Comput. Chem., 2007
2006
Using pseudo amino acid composition to predict protein structural classes: Approached with complexity measure factor.
J. Comput. Chem., 2006
Heuristic molecular lipophilicity potential (HMLP): Lipophilicity and hydrophilicity of amino acid side chains.
J. Comput. Chem., 2006
Prediction of protein subcellular location using hydrophobic patterns of amino acid sequence.
Comput. Biol. Chem., 2006
2005
J. Chem. Inf. Model., 2005
Heuristic molecular lipophilicity potential (HMLP): A 2D-QSAR study to LADH of molecular family pyrazole and derivatives.
J. Comput. Chem., 2005
Bioinform., 2005
2004
J. Chem. Inf. Model., 2004
Predicting the linkage sites in glycoproteins using bio-basis function neural network.
Bioinform., 2004
Bioinform., 2004
2003
J. Comput. Chem., 2003
2002
J. Comput. Chem., 2002
Comput. Chem., 2002
Comput. Chem., 2002
Comput. Chem., 2002