Special Issue on Machine Learning in Tissue Engineering
Guest Editors
Jason L. Guo, Ph.D.
Stanford University
Lydia E. Kavraki, Ph.D.
Rice University
Antonios G. Mikos, Ph.D.
Rice University
Machine learning technologies have transformed processes of scientific discovery and data analysis across multiple disciplines, ranging from healthcare to drug development and structural biology. By uncovering complex relationships between chemical, biological, and physiological factors, machine learning has accelerated the development of new therapeutics, biomaterials, and predictive algorithms for treatment outcomes. Tissue engineering and other interdisciplinary fields have begun to take advantage of machine learning strategies to investigate cell-material interactions, scaffold fabrication parameters, structure-function relationships in biomaterials design, and numerous other phenomena within tissue engineering. By leveraging supervised learning algorithms, for instance, tissue engineers have investigated the optimal fabrication parameters for high-fidelity rapid prototyping of polymeric scaffolds. Other studies have utilized unsupervised learning methods to identify previously unknown correlations between the biochemical composition and architecture of engineered tissue. Ultimately, machine learning tools offer the exciting potential to transform how tissue engineers design and evaluate biomaterial- and cell-based constructs.
The focus of this special issue is to highlight the increasing body of work leveraging machine learning and deep learning strategies to design tissue engineered systems and uncover fundamental mechanisms in tissue biology, biomaterial synthesis, and scaffold fabrication.
The Guest Editors of this special issue will consider articles that provide insight from the breadth of research in tissue engineering focused on machine learning and deep learning approaches. Original research papers, methods papers, and reviews will be considered. Areas of interest include, but are not limited to:
- Biomaterial and polymer development using machine learning
- Machine learning models of cell-, material-, and tissue-mediated interactions
- Deep learning-based image analysis for tissue engineering
- Computational models and simulations of engineered tissues
- Structure-function relationships in biological and material systems
- Scaffold fabrication using machine learning tools
- In vivo assessment of implants and tissue engineered constructs using machine learning
Visit the Journal’s website for Instructions for Authors including manuscript types and guidelines.