scholarly journals Single‐cell systems biology, COVID ‐19, and vaccination

2021 ◽  
Author(s):  
Attila Tárnok
Keyword(s):  
Systems Biology ◽  
Single Cell ◽  
Cell Systems

scholarly journals Frontispiz: In Vivo Subcellular Mass Spectrometry Enables Proteo‐Metabolomic Single‐Cell Systems Biology in a Chordate Embryo Developing to a Normally Behaving Tadpole ( X. laevis )

2021 ◽  
Vol 133 (23) ◽  
Author(s):  
Camille Lombard‐Banek ◽  
Jie Li ◽  
Erika P. Portero ◽  
Rosemary M. Onjiko ◽  
Chase D. Singer ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Systems Biology ◽  
Single Cell ◽  
Cell Systems

scholarly journals Single-cell systems biology: Probing the basic unit of information flow

2018 ◽  
Vol 8 ◽  
pp. 7-15 ◽  
Author(s):  
Simona Patange ◽  
Michelle Girvan ◽  
Daniel R. Larson
Keyword(s):  
Systems Biology ◽  
Single Cell ◽  
Information Flow ◽  
Basic Unit ◽  
Cell Systems

scholarly journals The Trifecta of Single-Cell, Systems-Biology, and Machine-Learning Approaches

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 1098
Author(s):  
Taylor M. Weiskittel ◽  
Cristina Correia ◽  
Grace T. Yu ◽  
Choong Yong Ung ◽  
Scott H. Kaufmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systems Biology ◽  
Single Cell ◽  
Individualized Medicine ◽  
Learning Approaches ◽  
Imaging Analysis ◽  
Biological Mechanisms ◽  
Cell Systems ◽  
Cell Trajectory ◽  
Cell Data

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.

Sign in / Sign up

Export Citation Format

Share Document