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

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Classification of biomaterial using polarization of light at present having difficulty for label-free and direct optical detection. The optical properties of a sample which are profoundly explored through the absorption coefficient, scattering coefficient, anisotropy coefficient and degree of linear polarization (DoLP) are neither simple nor easy to handle. In this study, Angle of polarized light (AOP) is our biggest concern. Neither need labeling procedure nor hardly to measure. Instead of linear polarization, this study determined the angle of polarized light as a potent parameter for polarization measurement at the variation axis of transmitted polarized light. Hence, this work was mainly conducted to identify the angle of polarized light for classification of agarose sample, a three-dimensional crosslinked polymer. In this work, a photodiode acts as a polarized light sensor to read voltage changes due to variable concentrations of agarose samples. At the end of the study, relationships between the Angle of Polarized light (AOP) and concentrations of agarose sample at variation axis of transmitted light were successfully investigated. Our result demonstrated a linear correlation between measured voltage (mV) and the concentration of agarose sample (g/ml) with output polarization behavioral model (AOP, ϴ◦) at variation axis of transmitted light. This outcome concluded that the polarization property of the agarose sample in perspective of angle can be identified at variation axis of transmitted light. Therefore, our polarization measurement-setup with variation axis of light transmission is reliable to determine the polarization property of the unknown three-dimensional structure of tissue-mimicking phantom in the future.