Abstract
The study of cell biology is extremely complex involving the diverse structural, functional features, development, differentiation, metabolism, and regulation. While it is intriguing to know the features, functions, and regulations in a normal cell and it is also important to understand the deviations leading to disease phenotype. The study of extremely complex mechanisms involved in cell fate, biology of lineage specific differentiation from the pluripotent stem cell capable of differentiating into different cell types, is far from complete and it is also complex to understand the biology of the cancer cell. While wet lab based techniques and omics based sequencing studies have helped in our understanding in the intricate biology of the cell in health and disease, machine learning tools and computational biology have immense applications in in-depth study of the biology of the cell. In this chapter we focus of the diverse application of computation and machine learning tool in the study of the biology of the cell.
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Abbreviations
- CSC:
-
Cancer stem cell
- ESC:
-
Embryonic stem cells
- iPSC:
-
Induced pluripotent stem cells
- OCLR:
-
One-class logistic regression
- PSC:
-
Pluripotent stem cells
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Ghosh, S., Dasgupta, R. (2022). Applications of Machine Learning in Study of Cell Biology. In: Machine Learning in Biological Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-16-8881-2_22
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DOI: https://doi.org/10.1007/978-981-16-8881-2_22
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