Kerner Jacob, Dogan Alan, von Recum Horst
Case Western Reserve University; 10900 Euclid Ave., Cleveland Ohio 44106.
Acta Biomater. 2021 Aug;130:54-65. doi: 10.1016/j.actbio.2021.05.053. Epub 2021 Jun 1.
Machine learning have been widely adopted in a variety of fields including engineering, science, and medicine revolutionizing how data is collected, used, and stored. Their implementation has led to a drastic increase in the number of computational models for the prediction of various numerical, categorical, or association events given input variables. We aim to examine recent advances in the use of machine learning when applied to the biomaterial field. Specifically, quantitative structure properties relationships offer the unique ability to correlate microscale molecular descriptors to larger macroscale material properties. These new models can be broken down further into four categories: regression, classification, association, and clustering. We examine recent approaches and new uses of machine learning in the three major categories of biomaterials: metals, polymers, and ceramics for rapid property prediction and trend identification. While current research is promising, limitations in the form of lack of standardized reporting and available databases complicates the implementation of described models. Herein, we hope to provide a snapshot of the current state of the field and a beginner's guide to navigating the intersection of biomaterials research and machine learning. STATEMENT OF SIGNIFICANCE: Machine learning and its methods have found a variety of uses beyond the field of computer science but have largely been neglected by those in realm of biomaterials. Through the use of more computational methods, biomaterials development can be expediated while reducing the need for standard trial and error methods. Within, we introduce four basic models that readers can potentially apply to their current research as well as current applications within the field. Furthermore, we hope that this article may act as a "call to action" for readers to realize and address the current lack of implementation within the biomaterials field.
机器学习已在包括工程、科学和医学在内的各种领域中广泛应用，彻底改变了数据的收集、使用和存储方式。它们的应用导致了用于预测给定输入变量的各种数值、分类或关联事件的计算模型数量急剧增加。我们旨在研究机器学习应用于生物材料领域的最新进展。具体而言，定量结构-性质关系提供了将微观分子描述符与更大的宏观材料性质相关联的独特能力。这些新模型可进一步细分为四类：回归、分类、关联和聚类。我们研究了机器学习在生物材料三大类（金属、聚合物和陶瓷）中的最新方法和新用途，以进行快速性能预测和趋势识别。虽然当前的研究很有前景，但缺乏标准化报告和可用数据库形式的局限性使所描述模型的实施变得复杂。在此，我们希望提供该领域当前状态的概述以及生物材料研究与机器学习交叉领域的入门指南。重要性声明：机器学习及其方法已在计算机科学领域之外有多种用途，但在生物材料领域却 largely被忽视。通过使用更多计算方法，可以加快生物材料的开发，同时减少对标准试错方法的需求。在本文中，我们介绍了四种基本模型，读者可以将其潜在地应用于他们当前的研究以及该领域内的当前应用。此外，我们希望本文可以作为一种“行动呼吁”，促使读者认识并解决生物材料领域当前缺乏实施的问题。
