Abstract
One of the most intriguing and fundamental questions in developmental biology is in the domain of understanding of cell fate, molecular mechanisms, regulatory factors, signaling molecules, key molecules and networks involved in the process of decision-making, and cell fate determination and differentiation. It is also intriguing to know the phenomena of differentiation and dedifferentiation and the molecular mechanisms involved. Recently high-throughput based technologies, omics based approaches and advances in line cell imaging and computational tools and machine learning algorithms which is a develo** domain with applications in developmental biology have enabled study of cells at a single-cell level and could be a step towards understanding of cell fate choices and also in understanding the aberrant phenotype and behavior of cell at the initiation of biology of disease like cancer. We discuss in this chapter the application of machine learning in the study of cell fate.
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Abbreviations
- C elegans :
-
Caenorhabditis elegans
- D melanogaster :
-
Drosophila melanogaster
- D rerio :
-
Danio rerio
- HSC:
-
Hematopoietic stem cells
- KO:
-
Knock-outs
- M. musculus :
-
Mus musculus
- mESC :
-
Mouse embryonic stem cell
- SVM:
-
Support vector machine
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Ghosh, S., Dasgupta, R. (2022). Cell Fate Analysis and Machine Learning. In: Machine Learning in Biological Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-16-8881-2_24
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DOI: https://doi.org/10.1007/978-981-16-8881-2_24
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