A User’s Guide to Machine Learning for Polymeric Biomaterials

作者：Travis A. Meyer, César E. Ramírez, Matthew Tamasi, Adam J. Gormley · 发表于：ACS Polymers Au · 年份：2022 · DOI：10.1021/acspolymersau.2c00037 · 被引用次数：94 · 研究领域：Machine Learning in Materials Science、Cell Image Analysis Techniques、3D Printing in Biomedical Research

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.