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Therapeutic Targets: Progress of Their Exploration and Investigation of Their Characteristics
- C. Zheng, L. Y. Han, C. Yap, Z. Ji, Z. Cao, Y. Z. Chen
- Biology, ChemistryPharmacological Reviews
- 1 June 2006
The characteristics of the currently explored targets are investigated to analyze their sequence, structure, family representation, pathway association, tissue distribution, and genome location features for finding clues useful for searching for new targets.
Database of traditional Chinese medicine and its application to studies of mechanism and to prescription validation
Traditional Chinese Medicine (TCM) is widely practised and is viewed as an attractive alternative to conventional medicine. Quantitative information about TCM prescriptions, constituent herbs and…
PEARLS: Program for Energetic Analysis of Receptor-Ligand System
A substantial degree of correlation between the computed free energy and experimental binding affinity was found, which suggests that PEARLS may be useful in facilitating energetic analysis of ligand-protein,ligand-nucleic acid, and protein-n nucleic acid interactions.
Predicting functional family of novel enzymes irrespective of sequence similarity: a statistical learning approach
- L. Y. Han, C. Cai, Z. Ji, Z. Cao, J. Cui, Y. Z. Chen
- Biology, Computer ScienceNucleic acids research
- 7 December 2004
Here SVM is tested for functional family assignment of two groups of enzymes, suggesting that it is potentially useful for facilitating functional study of novel proteins.
Protein function classification via support vector machine approach.
In Silico Prediction of Pregnane X Receptor Activators by Machine Learning Approache
Three machine learning methods for predicting PXR activators were explored, which were trained and tested by using significantly higher number of compounds, 128 PXr activators (98 human and 77 PxR non-activators), than those of previous studies.
Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties.
Prediction of transporter family from protein sequence by support vector machine approach
The study suggests that the SVM is potentially useful for facilitating functional study of transporters irrespective of sequence similarity, and methods for predicting TC family without sequence alignments or clustering are particularly useful.
Synergistic therapeutic actions of herbal ingredients and their mechanisms from molecular interaction and network perspectives.