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Identifying Critical State of Complex Diseases by Single-Sample-Based Hidden Markov Model

https://doi.org/10.3389/fgene.2019.00285
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29/29 checkable references clean · checked 2026-07-24

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

2 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 29 checked references that resolve
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Identifying critical differentiation state of MCF-7 cells for breast cancer by dynamical network biomarkers
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MicroRNA-146a Suppresses Metastatic Activity in Brain Metastasis
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Epileptic Seizures May Begin Hours in Advance of Clinical Onset
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Identifying early-warning signals of critical transitions with strong noise by dynamical network markers
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Identifying critical transitions and their leading biomolecular networks in complex diseases
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Hunt for the tipping point during endocrine resistance process in breast cancer by dynamic network biomarkers
resolves10.1002/med.21293
Early Diagnosis of Complex Diseases by Molecular Biomarkers, Network Biomarkers, and Dynamical Network Biomarkers
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Identifying critical transitions of complex diseases based on a single sample
resolves10.1101/gad.1824809
ADAMTS1 and MMP1 proteolytically engage EGF-like ligands in an osteolytic signaling cascade for bone metastasis
resolves10.1038/nm0303-241
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Mathematical Biology
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Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process
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The 2 references without a DOI — listed, not checked
no DOI — not checkedA gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models
no DOI — not checkedStomach Cancer Treatment Choices by Type and Stage of Stomach Cancer
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