Computational intelligence in protein-ligand interaction analysis /
Computational Intelligence in Protein-Ligand Interaction Analysis presents computational techniques for predicting protein-ligand interactions, recognizing protein interaction sites, and identifying protein drug targets.
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| Другие авторы: | , , |
|---|---|
| Формат: | eКнига |
| Язык: | английский |
| Опубликовано: |
[S.l.] :
Academic Press,
2024.
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| Предметы: | |
| Online-ссылка: | ScienceDirect |
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| LEADER | 00000cam a2200000 a 4500 | ||
|---|---|---|---|
| 001 | on1428131698 | ||
| 003 | OCoLC | ||
| 005 | 20260915095210.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 240328s2024 enk o 000 0 eng d | ||
| 040 | |a YDX |b eng |c YDX |d OPELS |d OCLCO |d EBLCP |d OCLCQ |d UKKRT |d SFB |d OCLCQ |d OCLCL |d OCLCF | ||
| 020 | |a 9780128244357 |q (electronic bk.) | ||
| 020 | |a 0128244356 |q (electronic bk.) | ||
| 020 | |z 9780128243862 | ||
| 020 | |z 0128243864 | ||
| 035 | |a (OCoLC)1428131698 | ||
| 050 | 4 | |a QP517.P76 | |
| 082 | 0 | 4 | |a 572.6 |2 23/eng/20240404 |1 https://id.oclc.org/worldcat/ddc/E3GyxVtG98FMGK6kPTRq7XkCGX |
| 245 | 0 | 0 | |a Computational intelligence in protein-ligand interaction analysis / |c edited by Bing Wang, Peng Chen, Jun Zhang. |
| 260 | |a [S.l.] : |b Academic Press, |c 2024. | ||
| 300 | |a 1 online resource | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 520 | |a Computational Intelligence in Protein-Ligand Interaction Analysis presents computational techniques for predicting protein-ligand interactions, recognizing protein interaction sites, and identifying protein drug targets. | ||
| 650 | 0 | |a Protein binding. | |
| 650 | 0 | |a Ligand binding (Biochemistry) | |
| 650 | 0 | |a Computational intelligence. | |
| 650 | 6 | |a Protéines |x Fixation. |0 (CaQQLa)201-0031406 | |
| 650 | 6 | |a Ligands (Biochimie) |x Fixation. |0 (CaQQLa)201-0092586 | |
| 650 | 6 | |a Intelligence informatique. |0 (CaQQLa)201-0265216 | |
| 650 | 7 | |a Computational intelligence. |2 fast |0 (OCoLC)fst00871995 | |
| 650 | 7 | |a Ligand binding (Biochemistry) |2 fast |0 (OCoLC)fst00998460 | |
| 650 | 7 | |a Protein binding. |2 fast |0 (OCoLC)fst01079673 | |
| 700 | 1 | |a WANG, BING. | |
| 700 | 1 | |a Chen, Peng. | |
| 700 | 1 | |a Zhang, Jun. | |
| 776 | 0 | 8 | |c Original |z 0128243864 |z 9780128243862 |w (OCoLC)1237632868 |
| 776 | 0 | 8 | |i Print version: |a Wang, Bing. |t Computational intelligence in protein-ligand interaction analysis. |d Amsterdam : Academic Press, 2023 |z 9780128243862 |w (OCoLC)1377213505 |
| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780128243862 |