In this white paper, we present how advances in machine learning are influencing the field of precision medicine. More specifically, we explain how Alchemite™ has been used to optimize and personalize stem cell treatment strategies for cartilage damage. Alchemite™ enables:
Advances in machine learning are opening a new chapter in precision medicine. Usually, the limitations of the available data and computational shortcomings render the task of predicting therapeutic efficacy from clinical trials and animal studies information highly challenging. However, the machine learning approach presented in this white paper, based on the Alchemite™ platform, provides significant insights into the optimization and personalisation of treatment strategies. That is exemplified here with the use case of cartilage damage, which affects the life quality of hundreds of millions of people, causing chronic joint pain and disability. Stem cell therapy has emerged as a promising treatment to repair cartilage and reduce pain. Identifying the best treatment using computational methods will enable to target therapeutic treatment and improve the patient’s chances of successful recovery.
The work presented in this white paper has been published as a peer-reviewed article in the PLoS Computational Biology:
Liu YYF, Lu Y, Oh S, Conduit GJ (2020) Machine learning to predict mesenchymal stem cell efficacy for cartilage repair. PLoS Comput Biol 16(10): e1008275. https://doi.org/10.1371/journal.pcbi.1008275