Meyer Travis A, Ramirez Cesar, Tamasi Matthew J, Gormley Adam J
Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States.
ACS Polym Au. 2022 Nov 17;3(2):141-157. doi: 10.1021/acspolymersau.2c00037. eCollection 2023 Apr 12.
The development of novel biomaterials is a challenging process, complicated by a design space with high dimensionality. Requirements for performance in the complex biological environment lead to difficult rational design choices and time-consuming empirical trial-and-error experimentation. Modern data science practices, especially artificial intelligence (AI)/machine learning (ML), offer the promise to help accelerate the identification and testing of next-generation biomaterials. However, it can be a daunting task for biomaterial scientists unfamiliar with modern ML techniques to begin incorporating these useful tools into their development pipeline. This Perspective lays the foundation for a basic understanding of ML while providing a step-by-step guide to new users on how to begin implementing these techniques. A tutorial Python script has been developed walking users through the application of an ML pipeline using data from a real biomaterial design challenge based on group's research. This tutorial provides an opportunity for readers to see and experiment with ML and its syntax in Python. The Google Colab notebook can be easily accessed and copied from the following URL: www.gormleylab.com/MLcolab.
新型生物材料的研发是一个充满挑战的过程，其设计空间具有高维度，使得这一过程变得复杂。在复杂生物环境中的性能要求导致了艰难的合理设计选择以及耗时的经验性试错实验。现代数据科学实践，尤其是人工智能（AI）/机器学习（ML），有望帮助加速下一代生物材料的识别和测试。然而，对于不熟悉现代ML技术的生物材料科学家而言，将这些有用工具纳入其研发流程可能是一项艰巨的任务。本观点文章奠定了对ML基本理解的基础，同时为新用户提供了一份关于如何开始应用这些技术的分步指南。基于该团队的研究，已经开发了一个教程Python脚本，引导用户使用来自实际生物材料设计挑战的数据来应用ML流程。本教程为读者提供了一个机会，让他们在Python中查看和试验ML及其语法。可以通过以下网址轻松访问和复制Google Colab笔记本：www.gormleylab.com/MLcolab。
