Computational Methods for Drug Repurposing /
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY :
Springer New York : Imprint: Humana,
2019.
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| Edition: | 1st ed. 2019. |
| Series: | Methods in Molecular Biology,
1903 |
| Subjects: |
Table of Contents:
- Methods for Discovering and Targeting Druggable Protein-Protein Interfaces and Their Application to Repurposing
- Performing an In Silico Repurposing of Existing Drugs by Combining Virtual Screening and Molecular Dynamics Simulation
- Repurposing Drugs Based on Evolutionary Relationships between Targets of Approved Drugs and Proteins of Interest
- Drug Repositioning by Mining Adverse Event Data in ClinicalTrials.gov
- Transcriptomic Data Mining and Repurposing for Computational Drug Discovery
- Network-Based Drug-Repositioning: Approaches, Resources, and Research Directions
- A Computational Bipartite-Graph-Based Drug Repurposing Method
- Implementation of a Pipeline Using Disease-Disease Associations for Computational Drug Repurposing
- An Application of Computational Drug Repurposing Based on Transcriptomic Signatures
- Drug-Induced Expression-Based Computational Repurposing of Small Molecules Affecting Transcription Factor Activity
- A Drug Repurposing Method Based on Drug-Drug Interaction Networks and Using Energy Model Layouts
- Integrating Biological Networks for Drug Target Prediction and Prioritization
- Using Drug Expression Profiles and Machine Learning Approach for Drug Repurposing
- Computational Prediction of Drug-Target Interactions via Ensemble Learning
- A Machine-Learning-Based Drug Repurposing Approach Using Baseline Regularization
- Machine Learning Approach for Predicting New Uses of Existing Drugs and Evaluation of Their Reliabilities
- A Drug-Target Network-Based Supervised Machine Learning Repurposing Method Allowing the Use of Multiple Heterogeneous Information Sources
- Heter-LP: A Heterogeneous Label Propagation Method for Drug Repositioning
- Tripartite Network-Based Repurposing Method Using Deep Learning to Compute Similarities for Drug-Target Prediction.