• Publications
  • Influence
Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence
Sequence-derived structural and physicochemical features have been extensively used for analyzing and predicting structural, functional, expression and interaction profiles of proteins and peptides.Expand
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Structural basis of α-scorpion toxin action on Nav channels
How activation leads to gating Voltage-gated sodium (Nav) channels are key players in electrical signaling. Central to their function is fast inactivation, and mutants that impede this causeExpand
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Variation of photosynthetic capacity with leaf age in an alpine orchid, Cypripedium flavum
Photosynthetic rate, chlorophyll fluorescence, leaf nitrogen and chlorophyll content of Cypripedium flavum were studied at different leaf ages. The photosynthetic capacity changed significantly withExpand
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A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor.
Support vector machines (SVM) and other machine-learning (ML) methods have been explored as ligand-based virtual screening (VS) tools for facilitating lead discovery. While exhibiting good hitExpand
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Assessment of approximate string matching in a biomedical text retrieval problem
TLDR
This work tests the usefulness of the Smith-Waterman algorithm with affine gap penalty as a method for biomedical literature retrieval. Expand
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Prediction of genotoxicity of chemical compounds by statistical learning methods.
  • H. Li, C. Ung, +4 authors Y. Chen
  • Computer Science, Medicine
  • Chemical research in toxicology
  • 4 June 2005
TLDR
This work is intended for testing several statistical learning methods by using 860 GT+ and GT- agents, which include support vector machines (SVM), probabilistic neural network (PNN), k-nearest neighbor (k-NN), and C4.5 decision tree (DT). Expand
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Quantitative structure-pharmacokinetic relationships for drug clearance by using statistical learning methods.
TLDR
Three statistical learning methods, general regression neural network (GRNN), support vector regression (SVR) and k-nearest neighbour (KNN) were explored for modeling the CL(tot) of all of the 503 known compounds. Expand
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Prediction of compounds with specific pharmacodynamic, pharmacokinetic or toxicological property by statistical learning methods.
Computational methods for predicting compounds of specific pharmacodynamic, pharmacokinetic, or toxicological property are useful for facilitating drug discovery and drug safety evaluation. TheExpand
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Application of support vector machines to in silico prediction of cytochrome p450 enzyme substrates and inhibitors.
Cytochrome P450 enzymes are responsible for phase I metabolism of the majority of drugs and xenobiotics. Identification of the substrates and inhibitors of these enzymes is important for the analysisExpand
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