Biomedical data science

Biomedical data science is a multidisciplinary field which leverages large volumes of data to promote biomedical innovation and discovery. Biomedical data science draws from various fields including Biostatistics, Biomedical informatics, and machine learning, with the goal of understanding biological and medical data. It can be viewed as the study and application of data science to solve biomedical problems.^[1] Modern biomedical datasets often have specific features which make their analyses difficult, including:

Large numbers of feature (sometimes billions), typically far larger than the number of samples (typically tens or hundreds)
Noisy and missing data
Privacy concerns (e.g., electronic health record confidentiality)
Requirement of interpretability from decision makers and regulatory bodies

Many biomedical data science projects apply machine learning to such datasets.^[2]^[3] These characteristics, while also present in many data science applications more generally, make biomedical data science a specific field. Examples of biomedical data science research include:

^ Altman, Russ; Levitt, Michael (2018). "What is Biomedical Data Science and Do We Need an Annual Review of It?". Annual Review of Biomedical Data Science. 1: i–iii. doi:10.1146/annurev-bd-01-041718-100001. S2CID 134950609.
^ Baldi, Pierre (2018). "Deep learning in biomedical data science". Annual Review of Biomedical Data Science. 1: 181–205. doi:10.1146/annurev-biodatasci-080917-013343. S2CID 67381478.
^ ^a ^b Ronneberger, Olaf; Fischer, Philipp; Brox, Thomas (2015). "U-net: Convolutional networks for biomedical image segmentation". International Conference on Medical Image Computing and Computer-Assisted Intervention. arXiv:1505.04597.
^ Duncan, James S; Insana, Michael F; Ayache, Nicholas (2020). "Biomedical imaging and analysis in the age of big data and deep learning [scanning the issue]". Proceedings of the IEEE. 108: 3–10. doi:10.1109/JPROC.2019.2956422. S2CID 210077608.
^ Su, Chang; Tong, Jie; Zhu, Yongjun; Cu, Peng; Wang, Fei (2020). "Network embedding in biomedical data science". Briefings in Bioinformatics. 21 (1): 182–197. doi:10.1093/bib/bby117. PMID 30535359.

[what-is-1] Altman, Russ; Levitt, Michael (2018). "What is Biomedical Data Science and Do We Need an Annual Review of It?". Annual Review of Biomedical Data Science. 1: i–iii. doi:10.1146/annurev-bd-01-041718-100001. S2CID 134950609.

[deep-learning-2] Baldi, Pierre (2018). "Deep learning in biomedical data science". Annual Review of Biomedical Data Science. 1: 181–205. doi:10.1146/annurev-biodatasci-080917-013343. S2CID 67381478.

[u-net-3] Ronneberger, Olaf; Fischer, Philipp; Brox, Thomas (2015). "U-net: Convolutional networks for biomedical image segmentation". International Conference on Medical Image Computing and Computer-Assisted Intervention. arXiv:1505.04597.

[4] Duncan, James S; Insana, Michael F; Ayache, Nicholas (2020). "Biomedical imaging and analysis in the age of big data and deep learning [scanning the issue]". Proceedings of the IEEE. 108: 3–10. doi:10.1109/JPROC.2019.2956422. S2CID 210077608.

[5] Su, Chang; Tong, Jie; Zhu, Yongjun; Cu, Peng; Wang, Fei (2020). "Network embedding in biomedical data science". Briefings in Bioinformatics. 21 (1): 182–197. doi:10.1093/bib/bby117. PMID 30535359.

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