Artificial image objects for classification of breast cancer biomarkers with transcriptome sequencing data and convolutional neural network algorithms

BACKGROUND: Transcriptome sequencing has been broadly available in clinical studies. However, it remains a challenge to utilize these data effectively for clinical applications due to the high dimension of the data and the highly correlated expression between individual genes. METHODS: We proposed a...

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Detalles Bibliográficos
Autores principales: Chen, Xiangning, Chen, Daniel G., Zhao, Zhongming, Balko, Justin M., Chen, Jingchun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8504079/
https://www.ncbi.nlm.nih.gov/pubmed/34629099
http://dx.doi.org/10.1186/s13058-021-01474-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8504079/
https://www.ncbi.nlm.nih.gov/pubmed/34629099
http://dx.doi.org/10.1186/s13058-021-01474-z

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