article Open AccessTop 1% cited
Identifying critical state of complex diseases by single-sample Kullback–Leibler divergence
BMC Genomics · 2020 · Vol. 21(1) · pp. 87–87
Jiayuan Zhong✉(South China University of Technology)Rui Liu(South China University of Technology)Pei Chen(South China University of Technology)
Abstract
The proposed method effectively explores and quantifies the disturbance on the background caused by a case sample, and thus characterizes the criticality of a biological system. Our method not only identifies the critical state or tipping point at a single sample level, but also provides the sKLD-signaling markers for further practical application. It is therefore of great potential in personalized pre-disease diagnosis.
Ecosystem dynamics and resilienceMental Health Research TopicsGene Regulatory Network AnalysisDivergence (linguistics)Kullback–Leibler divergenceBiologyComputational biologySample (material)Evolutionary biologyDNA microarrayGeneticsStatisticsMathematics
MeSH terms
Disease SusceptibilityHumansSeverity of Illness IndexReproducibility of ResultsBiomarkersDisease ProgressionComputational BiologyGene Ontology
Funding
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- National Outstanding Youth Science Fund Project of National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities
- Basic and Applied Basic Research Foundation of Guangdong Province
Citations
473
FWCI
32.32
field-weighted impact
References
75
Percentile
100%
vs. same field & year
Citations per year
References
<i>Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry and Engineering</i>
Physics Today · 1995 · 2,653 citations
Citation Network
How this paper connects to the literature. Drag to explore, click any node to open that paper.
