MLATE: Machine learning for predicting cell behavior on cardiac tissue engineering scaffolds

Abstract

Cardiovascular disease is one of the leading causes of mortality worldwide and is responsible for millions of deaths annually. One of the most promising approaches to deal with this problem, which has spread recently, is cardiac tissue engineering (CTE). Many researchers have tried developing scaffolds with different materials, cell lines, and fabrication methods to help regenerate heart tissue. Machine learning (ML) is one of the hottest topics in science and technology, revolutionizing many fields and changing our perspective on solving problems. As a result of using ML, some scientific issues have been resolved, including protein-folding, a challenging problem in biology that remained unsolved for 50 years. However, it is not well addressed in tissue engineering. An AI-based software was developed by our group called MLATE (Machine Learning Applications in Tissue Engineering) to tackle tissue engineering challenges, which highly depend on conducting costly and time-consuming experiments. For the first time, to the best of our knowledge, a CTE scaffold dataset was created by collecting specifications from the literature, including different materials, cell lines, and fabrication methods commonly used in CTE scaffold development. These specifications were used as variables in the study. Then, the CTE scaffolds were rated based on cell behaviors such as cell viability, growth, proliferation, and differentiation on the scaffold on a scale of 0–3. These ratings were considered a function of the variables in the gathered dataset. It should be stated that this study was merely based on information available in the literature. Then, twenty-eight ML algorithms were applied to determine the most effective one for predicting cell behavior on CTE scaffolds fabricated by different materials, compositions, and methods. The results indicated the high performance of XGBoost with an accuracy of 87%. Also, by implementing ensemble learning algorithms and using five algorithms with the best performance, an accuracy of 93% with the AdaBoost Classifier and Voting Classifier was achieved. Finally, the open-source software developed in this study was made available for everyone by publishing the best model along with a step-by-step guide to using it online at: https://github.com/saeedrafieyan/MLATE.