Cargando…
Convolutional neural network for human cancer types prediction by integrating protein interaction networks and omics data
Many studies have proven the power of gene expression profile in cancer identification, however, the explosive growth of genomics data increasing needs of tools for cancer diagnosis and prognosis in high accuracy and short times. Here, we collected 6136 human samples from 11 cancer types, and integr...
Autores principales: | Chuang, Yi-Hsuan, Huang, Sing-Han, Hung, Tzu-Mao, Lin, Xiang-Yu, Lee, Jung-Yu, Lai, Wen-Sen, Yang, Jinn-Moon |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8526703/ https://www.ncbi.nlm.nih.gov/pubmed/34667236 http://dx.doi.org/10.1038/s41598-021-98814-y |
Ejemplares similares
-
A novel graph convolutional neural network for predicting interaction sites on protein kinase inhibitors in phosphorylation
por: Wang, Feiqi, et al.
Publicado: (2022) -
Multi-omics Data Integration Model Based on UMAP Embedding and Convolutional Neural Network
por: ElKarami, Bashier, et al.
Publicado: (2022) -
Contextual Integration in Cortical and Convolutional Neural Networks
por: Iyer, Ramakrishnan, et al.
Publicado: (2020) -
A universal framework for single-cell multi-omics data integration with graph convolutional networks
por: Gao, Hongli, et al.
Publicado: (2023) -
Erratum: Contextual Integration in Cortical and Convolutional Neural Networks
por: Frontiers Production Office,
Publicado: (2020)