Machine learning models for predicting drug toxicity and side effects
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Amit Sharma
Abstract
An extensive examination of machinemachine learning models created specifically to meet these challenges was provided in this chapter. The topic begins with a thorough analysis of the established techniques for predicting drug toxicity, emphasizing their drawbacks and the need for more advanced strategies. It then transitions to a detailed examination of various machine learning techniques, with a focus on supervised and unsupervised learning methods, including neural networks, autoencoders, and hybrid models. Emphasis was placed on the advantages these methods offer in improving prediction accuracy and robustness. Additionally, the chapter addresses crucial evaluation and validation techniques, with a particular focus on external validation, to assess model generalizability and reliability. Finally, ethical and regulatory considerations are discussed, underscoringunderscoring the importance of adhering to established standards to ensure responsible model deployment. The purpose of this chapter was to provide researchers and practitioners in the field with a comprehensive understanding of state-of-the-art machine learning techniques for drug toxicity prediction.
Abstract
An extensive examination of machinemachine learning models created specifically to meet these challenges was provided in this chapter. The topic begins with a thorough analysis of the established techniques for predicting drug toxicity, emphasizing their drawbacks and the need for more advanced strategies. It then transitions to a detailed examination of various machine learning techniques, with a focus on supervised and unsupervised learning methods, including neural networks, autoencoders, and hybrid models. Emphasis was placed on the advantages these methods offer in improving prediction accuracy and robustness. Additionally, the chapter addresses crucial evaluation and validation techniques, with a particular focus on external validation, to assess model generalizability and reliability. Finally, ethical and regulatory considerations are discussed, underscoringunderscoring the importance of adhering to established standards to ensure responsible model deployment. The purpose of this chapter was to provide researchers and practitioners in the field with a comprehensive understanding of state-of-the-art machine learning techniques for drug toxicity prediction.
Chapters in this book
- Frontmatter I
- Contents V
- List of contributors VII
- Blockchain technology to secure medical data sharing in machine learning applications ensure privacy and integrity 1
- AI-powered sensors and devices for sustained health tracking 39
- Development of AI-driven biomedical sensors and devices optimization for continuous health monitoring 89
- Design and development of AI-driven biomedical sensors and devices and their optimization for continuous health monitoring 131
- Machine learning-driven personalized medicine: customized drug delivery systems and patient-specific material applications 193
- Personalized medicine using customized drug delivery systems and patient-specific material solutions, enabled by machine learning algorithms 239
- AI-driven drug design exploring molecular docking and lead optimization using machine learning algorithms 297
- Machine learning models for predicting drug toxicity and side effects 335
- Machine learning innovations in biomedical materials from drug discovery to personalized medicine 395
- High-throughput screening for novel medical materials: machine learning-enabled approaches 445
- Automated materials characterization using machine learning for screening biocompatible materials 489
- Machine learning algorithms for enhanced medical image analysis and diagnostics 541
- Transforming healthcare with machine learning 585
- Revolutionizing healthcare 635
- Index 687
- De Gruyter Series in Advanced Mechanical Engineering
Chapters in this book
- Frontmatter I
- Contents V
- List of contributors VII
- Blockchain technology to secure medical data sharing in machine learning applications ensure privacy and integrity 1
- AI-powered sensors and devices for sustained health tracking 39
- Development of AI-driven biomedical sensors and devices optimization for continuous health monitoring 89
- Design and development of AI-driven biomedical sensors and devices and their optimization for continuous health monitoring 131
- Machine learning-driven personalized medicine: customized drug delivery systems and patient-specific material applications 193
- Personalized medicine using customized drug delivery systems and patient-specific material solutions, enabled by machine learning algorithms 239
- AI-driven drug design exploring molecular docking and lead optimization using machine learning algorithms 297
- Machine learning models for predicting drug toxicity and side effects 335
- Machine learning innovations in biomedical materials from drug discovery to personalized medicine 395
- High-throughput screening for novel medical materials: machine learning-enabled approaches 445
- Automated materials characterization using machine learning for screening biocompatible materials 489
- Machine learning algorithms for enhanced medical image analysis and diagnostics 541
- Transforming healthcare with machine learning 585
- Revolutionizing healthcare 635
- Index 687
- De Gruyter Series in Advanced Mechanical Engineering
