Machine Learning Challenges for Single Cell Data
Recent technological advances in the fields of biology and medicine allow measuring single cells into unprecedented depth. This results in new types of high-throughput datasets that shed new lights on cell development, both in healthy as well as diseased tissues. However, studying these biological processes into greater detail crucially depends on novel computational techniques that efficiently mine single cell data sets. In this paper, we introduce machine learning techniques for single cell data analysis: we summarize the main developments in the field, and highlight a number of interesting new avenues that will likely stimulate the design of new types of machine learning algorithms.
KeywordsBioinformatics Single cell analysis Machine learning
- 4.Aghaeepour, N., et al.: A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry A (2015). doi: 10.1002/cyto.a.22732
- 5.Van Gassen, S., Vens, C., Dhaene, T., Lambrecht, B.N., Saeys, Y.: FloReMi: flow density survival regression using minimal feature redundancy. Cytometry A (2015). doi: 10.1002/cyto.a.22734