Machine Learning Applications in Tissue Engineering (MLATE)

In this repository, we provide a subset of applications of AI in tissue engineering. In MLATE V1, we predicted cell responses for cardiac tissue engineering. In MLATE V2, we implemented a wide range of supervised and unsupervised algorithms for predicting the quality of 3D (bio)printed scaffolds. In MLATE V3, we extended this to a cross-tissue dataset and added Bayesian optimization of scaffold formulations, LLM-assisted protocol generation, and a web application.

You can read our papers using the following links. We would be more than happy if you cite our works if you use our codes and datasets.

MLATE V1

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

Saeed Rafieyan, Ebrahim Vasheghani-Farahani, Nafiseh Baheiraei, and Hamidreza Keshavarz | Computers in Biology and Medicine | 2023

https://doi.org/10.1016/j.compbiomed.2023.106804

MLATE V2

A Practical Machine Learning Approach for Predicting the Quality of 3D (Bio)Printed Scaffolds

Saeed Rafieyan, Elham Ansari, and Ebrahim Vasheghani-Farahani | Biofabrication | 2024 https://doi.org/10.1088/1758-5090/ad6374

MLATE V3

MLATE V3: An Open-Source Cross-Tissue AI Framework for Data-Driven Optimization of 3D-Printed and Bioprinted Scaffolds

Saeed Rafieyan et al. | Biofabrication | under review

Code, dataset and analysis pipeline: V3/ Web application: https://huggingface.co/spaces/Saeed/MLATE