Machine Learning in Stem Cells Research: Application for Biosafety and Bioefficacy Assessment

The applicability of machine learning-based analysis in the field biomedical has been very beneficial determining biological mechanism and validation for a wide range scenarios. This approach is also gaining momentum various stem cells research activities, specifically characterization differentiation pattern. adoption similar computational approaches to study assess biosafety bioefficacy risks clinical application next progression. In particular where tumorigenicity one major concerns therapy. There are many factors influencing which may be difficult capture under conventional laboratory settings. addition, given possible multifactorial etiology tumorigenicity, defining one-size-fits-all strategy test such risk might not feasible compromise safety effectiveness Given increase datasets (which no longer limited genomic data) advancement health informatics powered by state-of-the-art learning algorithms, there exists potential practical Here, we identified relevant suggested protocols intended focusing on possibility its usage assessment. Ultimately, generating models that assist healthcare professionals make better-informed decision cell