Centre of Education and Training Professions (CRMEF) of Marrakech-Safi, Marrakech, Morocco.
Methods Mol Biol. 2024;2753:159-180. doi: 10.1007/978-1-0716-3625-1_7.
Machine learning (ML) is a subfield of artificial intelligence (AI) that consists of developing algorithms that can automatically learn patterns and relationships from data, without being explicitly programmed. It continues to advance with the development of more sophisticated algorithms, increased computational power, and larger datasets, leading to significant advancements in AI technology. With the significant progress made in ML, the need to apply these systems in the area of teratogenicity is growing. It is sought as robust boosting methods to overcome many limitations and restrictions facing the experimental studies. By performing tasks such as classification, regression, clustering, anomaly detection, and decision systems, ML can be used to assess whether an agent is teratogen or not or to determine its teratogenic potential. It may also be used for the purpose of deciding on the use of medicinal products. In this chapter, we describe how ML can be used to investigate teratogenicity.
机器学习（ML）是人工智能（AI）的一个分支，它由开发可以自动从数据中学习模式和关系的算法组成，而无需进行显式编程。随着更复杂的算法、计算能力的提高和更大的数据集的发展，它在人工智能技术方面取得了显著的进展。随着 ML 的显著进步，将这些系统应用于致畸性领域的需求也在增长。它被寻求作为强大的增强方法来克服实验研究面临的许多限制和约束。通过执行分类、回归、聚类、异常检测和决策系统等任务，ML 可以用于评估一种药物是否具有致畸性或确定其致畸潜力。它也可以用于决定药品的使用。在本章中，我们描述了如何使用 ML 来研究致畸性。
