A Wrapper Based Feature Selection Method for ADMET Prediction Using Evolutionary Computing 1st Edition by Axel Soto, Rocio Cecchini, Gustavo Vazquez, Ignacio Ponzoni ISBN 9783540787570

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ISBN 10: 
ISBN 13: 9783540787570
Author: Axel Soto, Rocio Cecchini, Gustavo Vazquez, Ignacio Ponzoni

 A Wrapper Based Feature Selection Method for ADMET Prediction Using Evolutionary Computing 1st Table of contents:

• Introduction

  • 1.1 Background and Motivation
  • 1.2 Overview of ADMET Prediction
  • 1.3 Challenges in ADMET Prediction
  • 1.4 The Role of Feature Selection in Predictive Modeling
  • 1.5 Evolutionary Computing in Feature Selection
  • 1.6 Objective and Contributions of the Study
  • 1.7 Structure of the Paper

• Literature Review

  • 2.1 Overview of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) Prediction
  • 2.2 Machine Learning Approaches for ADMET Prediction
  • 2.3 Feature Selection Techniques in Predictive Modeling
  • 2.4 Wrapper-Based Feature Selection Methods
  • 2.5 Evolutionary Computing and Its Application in Feature Selection
  • 2.6 Existing Approaches for ADMET Prediction and Feature Selection

• Methodology

  • 3.1 Overview of Wrapper-Based Feature Selection
  • 3.2 Evolutionary Computing Techniques Used for Feature Selection
    • 3.2.1 Genetic Algorithms
    • 3.2.2 Genetic Programming
    • 3.2.3 Differential Evolution
  • 3.3 ADMET Prediction Models and Their Implementation
  • 3.4 Feature Evaluation Criteria and Fitness Function
  • 3.5 Model Training and Cross-Validation
  • 3.6 Evaluation Metrics for Feature Selection and ADMET Prediction

• Data Acquisition and Preprocessing

  • 4.1 ADMET Datasets Overview
  • 4.2 Data Collection and Data Sources
  • 4.3 Data Cleaning and Preprocessing Steps
    • 4.3.1 Handling Missing Values
    • 4.3.2 Data Normalization and Scaling
  • 4.4 Feature Extraction and Representation
  • 4.5 Dimensionality Reduction Methods

• Wrapper-Based Feature Selection Approach

  • 5.1 Design of the Wrapper-Based Feature Selection Framework
  • 5.2 Feature Subset Search Strategies Using Evolutionary Algorithms
  • 5.3 Fitness Evaluation for Feature Subsets
  • 5.4 Selection, Crossover, and Mutation Operators in Evolutionary Computing
  • 5.5 Incorporating ADMET Prediction Model in the Feature Selection Process
  • 5.6 Computational Complexity Considerations

• Experimental Setup and Results

  • 6.1 Experimental Setup and Tools Used
  • 6.2 ADMET Prediction Models and Algorithms Used (e.g., Random Forest, SVM, Neural Networks)
  • 6.3 Performance Comparison of Wrapper-Based Selection vs. Other Feature Selection Methods
  • 6.4 Evaluation on Benchmark Datasets
  • 6.5 Statistical Analysis of Results
    • 6.5.1 Accuracy, Sensitivity, Specificity, AUC-ROC
    • 6.5.2 Feature Selection Efficiency
  • 6.6 Impact of Feature Selection on ADMET Prediction Accuracy

• Discussion

  • 7.1 Analysis of Results
  • 7.2 Strengths and Limitations of the Wrapper-Based Approach
  • 7.3 Benefits of Evolutionary Computing in Feature Selection
  • 7.4 Comparison with Traditional Feature Selection Techniques
  • 7.5 Application of the Proposed Approach in Drug Discovery and Toxicity Prediction
  • 7.6 Future Improvements and Directions

• Conclusion

  • 8.1 Summary of Findings
  • 8.2 Contributions to ADMET Prediction and Feature Selection
  • 8.3 Implications for Drug Development and Toxicology Studies
  • 8.4 Final Remarks and Future Work

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