Research & Reviews: Journal of Engineering and Technology
MenuISSN: 2319-9873
ISSN: 2319-9873
+44 7456 035580 Yingying Zhou
NMPA Key Laboratory for Dental Materials, China
ScientificTracks Abstracts: RRJET
Current functional assessment of biomaterial-induced stem cell lineage fate in vitro mainly relies on biomarker-dependent methods with limited accuracy and efficiency. This study aims to evaluate the function of biomaterials in inducing stem cell differentiation with the transcriptome of hMSCs (human mesenchymal stem cells) as a quantitative basis. A gene expression reference and an intelligent assessment model would be established for hMSCs differentiation based on big data and machine learning. Methodology & Theoretical Orientation: A framework named â??Mesenchymal stem cell Differentiation Prediction (MeD-P)â? was reported for biomaterial-induced cell lineage fate prediction. MeD-P contains a cell-type-specific gene expression profile as a reference by integrating public RNA-seq data related to tri-lineage differentiation (osteogenesis, chondrogenesis, and adipogenesis) of human mesenchymal stem cells (hMSCs) and a predictive model for classifying hMSCs differentiation lineages using the k-nearest neighbors (kNN) strategy. Findings: MeD-P exhibits an overall accuracy of 90.63% on testing datasets, which is significantly higher than the model constructed based on canonical marker genes (80.21%). Moreover, evaluations of multiple biomaterials show that MeD-P provides accurate prediction of lineage fate on different types of biomaterials as early as the first week of hMSCs culture. Conclusion & Significance: MeD-P is an efficient and accurate strategy for stem cell lineage fate prediction. This study demonstrated that machine learning-based artificial intelligence strategies can be widely used for standardized functional biomaterial evaluation, which could hasten the progress of regenerative medicine research.
Yingying Zhou is a Ph.D candidate in Prosthodontics. Her research focuses on biological evaluation of regenerative biomaterials. She has passion in improving the health and wellbeing using regenerative medicine. The intelligent model MeD-P creates a new approach for biomaterials functional evaluation. It is open to all scientists. This work paves the way for improving healthcare in regenerative medicine using AI and machine learning.