Cargando…
Combining Neuroimaging and Omics Datasets for Disease Classification Using Graph Neural Networks
Both neuroimaging and genomics datasets are often gathered for the detection of neurodegenerative diseases. Huge dimensionalities of neuroimaging data as well as omics data pose tremendous challenge for methods integrating multiple modalities. There are few existing solutions that can combine both m...
Autores principales: | Chan, Yi Hao, Wang, Conghao, Soh, Wei Kwek, Rajapakse, Jagath C. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9168232/ https://www.ncbi.nlm.nih.gov/pubmed/35677355 http://dx.doi.org/10.3389/fnins.2022.866666 |
Ejemplares similares
-
Network-based integration of multi-omics data for clinical outcome prediction in neuroblastoma
por: Wang, Conghao, et al.
Publicado: (2022) -
Deep learning and multi-omics approach to predict drug responses in cancer
por: Wang, Conghao, et al.
Publicado: (2022) -
HUT: Hybrid UNet transformer for brain lesion and tumour segmentation
por: Soh, Wei Kwek, et al.
Publicado: (2023) -
Classification of Multiple Sclerosis Clinical Profiles via Graph Convolutional Neural Networks
por: Marzullo, Aldo, et al.
Publicado: (2019) -
FPGA implementations of neural networks
por: Omondi, Amos R, et al.
Publicado: (2006)