http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Computational intelligence in protein-ligand interaction analysis
Front Cover -- Computational Intelligence in Protein-Ligand Interaction Analysis -- Computational Intelligence in Protein-Ligand Interaction Analysis -- Copyright -- Contents -- Contributors -- 1 -- Random forest method for predicting protein ligand-binding residues -- 1. Introduction -- 2. Methods -- 2.1 Datasets -- 2.2 Binding site definition -- 2.3 Feature vector representation of a residue -- 2.4 Combine different sliding windows -- 2.5 Ensemble of random forest classifiers -- 2.6 Evaluation criteria -- 3. Results and discussion -- 3.1 Prediction results on CASP9
3.2 Prediction results on CASP8 -- 3.3 Comparison with other binding site prediction methods -- 3.4 Case studies -- 3.5 Discussion -- 4. Conclusions -- References -- 2 -- Encoders of protein residues for identifying protein-protein interacting residues -- 1. Introduction -- 2. Methods -- 2.1 Data set -- 2.2 Sliding window technique -- 2.3 Generation of residue profiles -- 2.4 SVM-SOM classifiers -- 2.5 Classifiers combination -- 2.6 Measures for performance evaluation -- 3. Results -- 3.1 Determination of the sliding window length -- 3.2 Prediction performance without SOM
3.3 Prediction performance with the use of SOM -- 3.4 Improvement by using evolutionary context of residues with respect to hydrophobicity -- 3.5 A biological case of improvement by classifier ensemble -- 3.6 Comparison with other methods -- 3.7 Blind test -- 4. Conclusions -- 5. Supplementary materials -- References -- 3 -- Ensemble method for the Identification of hotspot residues from protein sequences -- 1. Introduction -- 2. Methods -- 2.1 Data sets -- 2.2 Ensemble learning method -- 2.2.1 Feature selection -- 2.2.2 Classifier construction -- 2.3 Evaluation criteria -- 3. Results
3.1 Performance of ensemble classifiers on different M for auto-correlation function -- 3.2 Performance of different classifiers on ASEdb -- 3.3 Performance of our model on train and test sets -- 3.4 Comparison with other methods -- 3.5 Discussion -- 3.5.1 Feature selection algorithm -- 3.5.2 Feature correlation analysis -- 3.5.3 Descriptor cluster analysis -- 3.6 Case study -- 4. Conclusion -- References -- 4 -- Predicting protein interaction sites from unlabeled sample information based on a semi-supervised approach -- 1. Introduction -- 2. Methods -- 2.1 Dataset -- 2.2 Feature extraction
2.3 Semi-supervised models -- 2.3.1 MeanS3VM_mkl -- 2.3.2 Means3vm-iter -- 2.3.3 S4VM -- 2.4 Evaluation criteria -- 3. Results -- 3.1 Prediction performance of three semi-supervised models -- 3.2 Prediction performance comparison between supervised and semi-supervised SVM -- 3.3 Comparison with other approaches -- 3.4 Visualization of experimental results -- 4. Conclusion -- References -- 5 -- An XGBoost-based model to predict protein-protein interaction sites -- 1. Introduction -- 2. Materials and methods -- 2.1 Dataset -- 2.2 Feature extraction -- 2.3 Unbalanced datasets processing
edited by Bing Wang, Peng Chen, Jun Zhang.