Cargando…
Identifying Critical State of Complex Diseases by Single-Sample-Based Hidden Markov Model
The progression of complex diseases is generally divided as a normal state, a pre-disease state or tipping point, and a disease state. Developing individual-specific method that can identify the pre-disease state just before a catastrophic deterioration, is critical for patients with complex disease...
Autores principales: | Liu, Rui, Zhong, Jiayuan, Yu, Xiangtian, Li, Yongjun, Chen, Pei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6458292/ https://www.ncbi.nlm.nih.gov/pubmed/31019526 http://dx.doi.org/10.3389/fgene.2019.00285 |
Ejemplares similares
-
Identifying the critical state of cancers by single-sample Markov flow entropy
por: Liu, Juntan, et al.
Publicado: (2023) -
Identifying critical state of complex diseases by single-sample Kullback–Leibler divergence
por: Zhong, Jiayuan, et al.
Publicado: (2020) -
A hidden Markov model for haplotype inference for present-absent data of clustered genes using identified haplotypes and haplotype patterns
por: Wu, Jihua, et al.
Publicado: (2014) -
Quantifying critical states of complex diseases using single-sample dynamic network biomarkers
por: Liu, Xiaoping, et al.
Publicado: (2017) -
Uncovering ecological state dynamics with hidden Markov models
por: McClintock, Brett T., et al.
Publicado: (2020)