Cargando…
A Machine Learning Approach for Tracing Tumor Original Sites With Gene Expression Profiles
Some carcinomas show that one or more metastatic sites appear with unknown origins. The identification of primary or metastatic tumor tissues is crucial for physicians to develop precise treatment plans for patients. With unknown primary origin sites, it is challenging to design specific plans for p...
Autores principales: | Liang, Xin, Zhu, Wen, Liao, Bo, Wang, Bo, Yang, Jialiang, Mo, Xiaofei, Li, Ruixi |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7732438/ https://www.ncbi.nlm.nih.gov/pubmed/33330438 http://dx.doi.org/10.3389/fbioe.2020.607126 |
Ejemplares similares
-
A Deep Learning Framework to Predict Tumor Tissue-of-Origin Based on Copy Number Alteration
por: Liang, Ying, et al.
Publicado: (2020) -
Editorial: Machine Learning Approaches to Human Movement Analysis
por: Zago, Matteo, et al.
Publicado: (2021) -
A Neural Network Framework for Predicting the Tissue-of-Origin of 15 Common Cancer Types Based on RNA-Seq Data
por: He, Binsheng, et al.
Publicado: (2020) -
TOOme: A Novel Computational Framework to Infer Cancer Tissue-of-Origin by Integrating Both Gene Mutation and Expression
por: He, Binsheng, et al.
Publicado: (2020) -
Metabolic Modeling Combined With Machine Learning Integrates Longitudinal Data and Identifies the Origin of LXR-Induced Hepatic Steatosis
por: van Riel, Natal A. W., et al.
Publicado: (2021)