| scholarly article | Q13442814 |
| P819 | ADS bibcode | 2010PLoSO...5.9603H |
| P356 | DOI | 10.1371/JOURNAL.PONE.0009603 |
| P932 | PMC publication ID | 2836373 |
| P698 | PubMed publication ID | 20300175 |
| P5875 | ResearchGate publication ID | 42345723 |
| P50 | author | Kuo-Chen Chou | Q30069869 |
| Zhisong He | Q37620636 | ||
| P2093 | author name string | Jian Zhang | |
| Le-Le Hu | |||
| Yu-Dong Cai | |||
| Xiangyin Kong | |||
| Xiao-He Shi | |||
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| Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition. | Q51861064 | ||
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| Assessment of chemical libraries for their druggability | Q33211319 | ||
| The structure of phospholamban pentamer reveals a channel-like architecture in membranes | Q33911608 | ||
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| Some insights into protein structural class prediction | Q33946823 | ||
| Angiotensin I-converting enzyme gene polymorphism and drug response | Q34093075 | ||
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| Application of pseudo amino acid composition for predicting protein subcellular location: stochastic signal processing approach. | Q34246815 | ||
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| Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes | Q34650136 | ||
| Proteomics, networks and connectivity indices. | Q34754781 | ||
| Predicting the cofactors of oxidoreductases based on amino acid composition distribution and Chou's amphiphilic pseudo-amino acid composition | Q34777941 | ||
| Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image | Q34797295 | ||
| Predicting protein subcellular location using Chou's pseudo amino acid composition and improved hybrid approach | Q34804330 | ||
| Predicting subcellular localization of mycobacterial proteins by using Chou's pseudo amino acid composition. | Q34824529 | ||
| GPCR-CA: A cellular automaton image approach for predicting G-protein-coupled receptor functional classes | Q34889641 | ||
| Predicting lipase types by improved Chou's pseudo-amino acid composition | Q34902217 | ||
| Prediction of protein secondary structure content by using the concept of Chou's pseudo amino acid composition and support vector machine | Q34923445 | ||
| Prediction of subcellular localization of apoptosis protein using Chou's pseudo amino acid composition | Q34929926 | ||
| Prediction of cell wall lytic enzymes using Chou's amphiphilic pseudo amino acid composition | Q34974776 | ||
| Prediction of G-protein-coupled receptor classes based on the concept of Chou's pseudo amino acid composition: an approach from discrete wavelet transform. | Q34975455 | ||
| A guide to drug discovery: Target selection in drug discovery | Q35037671 | ||
| SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence | Q35208363 | ||
| Structural bioinformatics and its impact to biomedical science | Q35848051 | ||
| A sequence-coupled vector-projection model for predicting the specificity of GalNAc-transferase | Q36279283 | ||
| GLIDA: GPCR--ligand database for chemical genomics drug discovery--database and tools update | Q36454245 | ||
| CYP induction-mediated drug interactions: in vitro assessment and clinical implications | Q36485692 | ||
| The nuclear receptor superfamily and drug discovery | Q36558133 | ||
| Progress in computational approach to drug development against SARS. | Q36683902 | ||
| Inhibition and induction of human cytochrome P450 enzymes: current status | Q37212794 | ||
| Structural basis for ligand binding and specificity in adrenergic receptors: implications for GPCR-targeted drug discovery. | Q37281064 | ||
| Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. | Q37283297 | ||
| Structure of cytochrome p450s and personalized drug | Q37370400 | ||
| The classification and origins of protein folding patterns | Q37943219 | ||
| Low-frequency Fourier spectrum for predicting membrane protein types | Q38412821 | ||
| Hum-PLoc: a novel ensemble classifier for predicting human protein subcellular localization | Q38518166 | ||
| Seventy-five percent accuracy in protein secondary structure prediction | Q38558729 | ||
| Using cellular automata to generate image representation for biological sequences | Q39707473 | ||
| Digital coding of amino acids based on hydrophobic index | Q39823671 | ||
| Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach | Q39990279 | ||
| Support Vector Machines for Predicting Membrane Protein Types by Using Functional Domain Composition | Q40236404 | ||
| Predicting human safety: screening and computational approaches | Q40744635 | ||
| Recognition of a protein fold in the context of the Structural Classification of Proteins (SCOP) classification | Q41675248 | ||
| Predicting membrane protein types by the LLDA algorithm | Q41999602 | ||
| Investigation into adamantane-based M2 inhibitors with FB-QSAR. | Q43290216 | ||
| Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites | Q44469596 | ||
| An in-depth analysis of the biological functional studies based on the NMR M2 channel structure of influenza A virus | Q45388829 | ||
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| Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-Nearest Neighbor classifiers | Q46724131 | ||
| Coupling interaction between thromboxane A2 receptor and alpha-13 subunit of guanine nucleotide-binding protein | Q46741746 | ||
| Molecular modeling of two CYP2C19 SNPs and its implications for personalized drug design | Q46790756 | ||
| P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P433 | issue | 3 | |
| P407 | language of work or name | English | Q1860 |
| P304 | page(s) | e9603 | |
| P577 | publication date | 2010-03-11 | |
| P1433 | published in | PLOS One | Q564954 |
| P1476 | title | Predicting drug-target interaction networks based on functional groups and biological features | |
| P478 | volume | 5 |
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| Q93163692 | A comparative chemogenic analysis for predicting Drug-Target Pair via Machine Learning Approaches |
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| Q35253001 | Discriminating between lysine sumoylation and lysine acetylation using mRMR feature selection and analysis |
| Q38043428 | Drug interaction networks: an introduction to translational and clinical applications |
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