期刊論文
| 學年 | 112 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2024-05-27 |
| 作品名稱 | Recent Progress in Machine Learning Approaches for Predicting Carcinogenicity in Drug Development |
| 作品名稱（其他語言） | |
| 著者 | Ho, Trang-thi |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Expert Opinion on Drug Metabolism & Toxicology |
| 摘要 | Introduction This review explores the transformative impact of machine learning (ML) on carcinogenicity prediction within drug development. It discusses the historical context and recent advancements, emphasizing the significance of ML methodologies in overcoming challenges related to data interpretation, ethical considerations, and regulatory acceptance. Areas covered The review comprehensively examines the integration of ML, deep learning, and diverse artificial intelligence (AI) approaches in various aspects of drug development safety assessments. It explores applications ranging from early-phase compound screening to clinical trial optimization, highlighting the versatility of ML in enhancing predictive accuracy and efficiency. Expert opinion Through the analysis of traditional approaches such as in vivo rodent bioassays and in vitro assays, the review underscores the limitations and resource intensity associated with these methods. It provides expert insights into how ML offers innovative solutions to address these challenges, revolutionizing safety assessments in drug development. |
| 關鍵字 | Artificial intelligence;carcinogenicity prediction;drug development;machine learning;predictive modeling;safety assessment;toxicogenomics;computational toxicology |
| 語言 | en |
| ISSN | 1744-7607 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | GBR |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/125647 ) |