1. A Cancer Biologist's Primer on Machine Learning Applications in High‐Dimensional Cytometry
- Author
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Kara L. Davis, Pablo Domizi, Yu-Chen Lo, Garry P. Nolan, and Timothy J. Keyes
- Subjects
Proteomics ,0301 basic medicine ,Histology ,Computer science ,Target audience ,High dimensional ,Machine learning ,computer.software_genre ,Article ,Field (computer science) ,Pathology and Forensic Medicine ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Key terms ,Artificial Intelligence ,Neoplasms ,Humans ,Mass cytometry ,business.industry ,Computational Biology ,Cell Biology ,Variety (cybernetics) ,030104 developmental biology ,Conceptual framework ,030220 oncology & carcinogenesis ,Artificial intelligence ,business ,Cytometry ,computer - Abstract
The application of machine learning and artificial intelligence to high-dimensional cytometry data sets has increasingly become a staple of bioinformatic data analysis over the past decade. This is especially true in the field of cancer biology, where protocols for collecting multiparameter single-cell data in a high-throughput fashion are rapidly developed. As the use of machine learning methodology in cytometry becomes increasingly common, there is a need for cancer biologists to understand the basic theory and applications of a variety of algorithmic tools for analyzing and interpreting cytometry data. We introduce the reader to several keystone machine learning-based analytic approaches with an emphasis on defining key terms and introducing a conceptual framework for making translational or clinically relevant discoveries. The target audience consists of cancer cell biologists and physician-scientists interested in applying these tools to their own data, but who may have limited training in bioinformatics. © 2020 International Society for Advancement of Cytometry.
- Published
- 2020
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