Detecting the Critical States of Type 2 Diabetes Mellitus Based on Degree Matrix Network Entropy by Cross-Tissue Analysis.

Yang, Yingke; Tian, Zhuanghe; Song, Mengyao; Ma, Chenxin; Ge, Zhenyang; Li, Peiluan

Yang, Yingke; Tian, Zhuanghe; Song, Mengyao; Ma, Chenxin; Ge, Zhenyang; Li, Peiluan.

Afiliación

Yang Y; School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China.
Tian Z; School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China.
Song M; School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China.
Ma C; School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China.
Ge Z; College of Agriculture, Henan University of Science and Technology, Luoyang 471023, China.
Li P; School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China.

Entropy (Basel) ; 24(9)2022 Sep 05.

Article en En | MEDLINE | ID: mdl-36141135

Palabras clave

Type 2 diabetes mellitus (T2DM); critical state; dark genes; dynamic network biomarker (DNB); network entropy; sample specific network (SSN)

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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Entropy (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China

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