Open AccessDOI
Biomaterials by design: Harnessing data for future development
Kun Xue,FuKe Wang,Ady Suwardi,Ming-Yong Han,Peili Teo,Pei Wang,Shijie Wang,Enyi Ye,Zibiao Li,Xian Jun Loh +9 more
- Vol. 12, pp 100165
TLDR
In this paper, the authors discuss the recent work on the use of machine learning in the discovery and design of biomaterials, including new polymeric, metallic, ceramics, and nanomaterials.Abstract:
Biomaterials is an interdisciplinary field of research to achieve desired biological responses from new materials, regardless of material type. There have been many exciting innovations in this discipline, but commercialization suffers from a lengthy discovery to product pipeline, with many failures along the way. Success can be greatly accelerated by harnessing machine learning techniques to comb through large amounts of data. There are many potential benefits of moving from an unstructured empirical approach to a development strategy that is entrenched in data. Here, we discuss the recent work on the use of machine learning in the discovery and design of biomaterials, including new polymeric, metallic, ceramics, and nanomaterials, and how machine learning can interface with emerging use cases of 3D printing. We discuss the steps for closer integration of machine learning to make this exciting possibility a reality.read more
Citations
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Journal ArticleDOI
Emerging early diagnostic methods for acute kidney injury
Zuoxiu Xiao,Qiong Huang,Yuqi Yang,Min Liu,Qiaohui Chen,Jia Huang,Yuting,Xiang,Xingyu Long,Tianjiao Zhao,Xin Wang,Xiao-Jin Zhu,Shiqi Tu,Yayun,Nan,Kelong Ai +15 more
TL;DR: This review comprehensively summarizes the application of machine learning in AKI prediction algorithms and specific scenarios, and introduces the key role of early biomarkers in the progress of AKI, and comprehensively summarize theApplication of emerging detection technologies for early AKI.
Journal ArticleDOI
A User’s Guide to Machine Learning for Polymeric Biomaterials
TL;DR: The Google Colab notebook as discussed by the authors provides a step-by-step guide to the use of machine learning in biomaterials development, using data from a real biomaterial design challenge based on group's research.
Journal ArticleDOI
Potential of Recycled Silicon and Silicon-Based Thermoelectrics for Power Generation
S. S. F. Duran,Danwei Zhang,Wei Yang Lim,Jing Cao,Hongfei Liu,Qiang Zhu,Chee Kiang Ivan Tan,Jianwei Xu,Xian Jun Loh,Ady Suwardi +9 more
TL;DR: In this paper , the authors summarize the usage of high-temperature thermoelectric generators (TEGs) in applications such as commercial aviation and space voyages, which can be used to guide sustainable recycling of e-waste into TEGs for power harvesting.
Journal ArticleDOI
Bottom-up design of hydrogels for programmable drug release.
TL;DR: In this paper , the authors present a review of physical models of hydrogel release and discuss the interesting potential and challenges for programming release, and potential implications with the advent of machine learning.
Journal ArticleDOI
Machine Learning in Tissue Engineering
TL;DR: A recent review as discussed by the authors highlights the novel methodologies, emerging strategies, and areas of potential growth within this rapidly evolving area of research, including machine-optimized biomaterial design, predictive modeling of scaffold fabrication, and spatiotemporal analysis of cell and tissue systems.
References
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Jessica Vamathevan,Dominic Clark,Paul Czodrowski,Ian Dunham,Edgardo Ferran,George Lee,Bin Li,Anant Madabhushi,Anant Madabhushi,Parantu K. Shah,Michaela Spitzer,Shanrong Zhao +11 more
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The Human Genome Project: Lessons from Large-Scale Biology
TL;DR: The Human Genome Project has been the first major foray of the biological and medical research communities into “big science” and many of the lessons learned will be applicable to future large-scale projects in biology.
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Biodegradable synthetic polymers: Preparation, functionalization and biomedical application
TL;DR: This review presents a comprehensive introduction to various types of synthetic biodegradable polymers with reactive groups and bioactive groups, and further describes their structure, preparation procedures and properties.
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3D printing of ceramics: A review
Zhangwei Chen,Ziyong Li,Junjie Li,Chengbo Liu,Changshi Lao,Yuelong Fu,Changyong Liu,Li Yang,Pei Wang,He Yi +9 more
TL;DR: A review on the latest advances in the 3D printing of ceramics and present the historical origins and evolution of each related technique is presented in this paper. And the main technical aspects, including feedstock properties, process control, post-treatments and energy source-material interactions, are also discussed.
Journal ArticleDOI
New developments in the Inorganic Crystal Structure Database (ICSD): accessibility in support of materials research and design.
TL;DR: The Inorganic Crystal Structure Database (ICSD) as discussed by the authors is a comprehensive collection of more than 60,000 crystal structure entries for inorganic materials and is produced cooperatively by Fachinformationszentrum Karlsruhe (FIZ), Germany, and the US National Institute of Standards and Technology (NIST).