The Trifecta of Single-Cell, Systems-Biology, and Machine-Learning Approaches.
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Cited by: 2


Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has revolutionized biomedical imaging analysis, drug discovery, and systems biology, is an ideal strategy to fill these gaps in single-cell studies. Machine learning additionally has proven to be remarkably synergistic with single-cell data because it remedies unique challenges while capitalizing on the positive aspects of single-cell data. In this review, we describe how systems-biology algorithms have layered machine learning with biological components to provide systems level analyses of single-cell omics data, thus elucidating complex biological mechanisms. Accordingly, we highlight the trifecta of single-cell, systems-biology, and machine-learning approaches and illustrate how this trifecta can significantly contribute to five key areas of scientific research: cell trajectory and identity, individualized medicine, pharmacology, spatial omics, and multi-omics. Given its success to date, the systems-biology, single-cell omics, and machine-learning trifecta has proven to be a potent combination that will further advance biomedical research.


Spatial Transcriptomics
Spatial Omics
machine learning
single-cell omics
single-cell systems biology
systems biology

MeSH terms

Computational Biology
Drug Discovery
High-Throughput Screening Assays
Machine Learning
Precision Medicine
Single-Cell Analysis
Systems Biology


Weiskittel, Taylor M
Correia, Cristina
Yu, Grace T
Ung, Choong Yong
Kaufmann, Scott H
Billadeau, Daniel D
Li, Hu

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