Identifying critical transitions and their leading biomolecular networks in complex diseases.
about
Network biomarkers reveal dysfunctional gene regulations during disease progressionPathway mapping and development of disease-specific biomarkers: protein-based network biomarkersDetecting early-warning signals for influenza A pandemic based on protein dynamical network biomarkers.Network-Based Biomedical Data Analysis.Measuring intratumor heterogeneity by network entropy using RNA-seq data.Network biomarkers, interaction networks and dynamical network biomarkers in respiratory diseases.The dynamics of DNA methylation covariation patterns in carcinogenesis.Detecting early-warning signals of type 1 diabetes and its leading biomolecular networks by dynamical network biomarkers.Increased firing irregularity as an emergent property of neural-state transition in monkey prefrontal cortexConditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks.Differential variability and correlation of gene expression identifies key genes involved in neuronal differentiation.Identifying critical differentiation state of MCF-7 cells for breast cancer by dynamical network biomarkers.The decrease of consistence probability: at the crossroad of catastrophic transition of a biological systemIdentifying early-warning signals of critical transitions with strong noise by dynamical network markers.Modular transcriptional repertoire and MicroRNA target analyses characterize genomic dysregulation in the thymus of Down syndrome infants.Thermodynamic measures of cancer: Gibbs free energy and entropy of protein-protein interactionsTowards dynamical network biomarkers in neuromodulation of episodic migraine.Transittability of complex networks and its applications to regulatory biomolecular networks.Quantifying critical states of complex diseases using single-sample dynamic network biomarkers.Defining and characterizing the critical transition state prior to the type 2 diabetes disease.Topological robustness analysis of protein interaction networks reveals key targets for overcoming chemotherapy resistance in glioma.Grip on health: A complex systems approach to transform health care.Diagnosing phenotypes of single-sample individuals by edge biomarkers.Forecasting influenza A pandemic outbreak using protein dynamical network biomarkers.Low-Grade Dysplastic Nodules Revealed as the Tipping Point during Multistep Hepatocarcinogenesis by Dynamic Network Biomarkers.Prediction and early diagnosis of complex diseases by edge-network.Discovering a critical transition state from nonalcoholic hepatosteatosis to nonalcoholic steatohepatitis by lipidomics and dynamical network biomarkers.Individual-specific edge-network analysis for disease prediction.Detecting the tipping points in a three-state model of complex diseases by temporal differential networks.Towards a critical transition theory under different temporal scales and noise strengths.Understanding migraine using dynamic network biomarkers.Detecting Early Warning Signal of Influenza A Disease Using Sample-Specific Dynamical Network Biomarkers.
P2860
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P2860
Identifying critical transitions and their leading biomolecular networks in complex diseases.
description
2012 nî lūn-bûn
@nan
2012年の論文
@ja
2012年学术文章
@wuu
2012年学术文章
@zh-cn
2012年学术文章
@zh-hans
2012年学术文章
@zh-my
2012年学术文章
@zh-sg
2012年學術文章
@yue
2012年學術文章
@zh
2012年學術文章
@zh-hant
name
Identifying critical transitio ...... networks in complex diseases.
@ast
Identifying critical transitio ...... networks in complex diseases.
@en
type
label
Identifying critical transitio ...... networks in complex diseases.
@ast
Identifying critical transitio ...... networks in complex diseases.
@en
prefLabel
Identifying critical transitio ...... networks in complex diseases.
@ast
Identifying critical transitio ...... networks in complex diseases.
@en
P2093
P2860
P356
P1433
P1476
Identifying critical transitio ...... networks in complex diseases.
@en
P2093
P2860
P2888
P356
10.1038/SREP00813
P407
P577
2012-12-10T00:00:00Z