Joint analysis of multiple high-dimensional data types using sparse matrix approximations of rank-1 with applications to ovarian and liver cancer

BACKGROUND: Technological advances enable the cost-effective acquisition of Multi-Modal Data Sets (MMDS) composed of measurements for multiple, high-dimensional data types obtained from a common set of bio-samples. The joint analysis of the data matrices associated with the different data types of a...

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Detalles Bibliográficos
Autores principales: Okimoto, Gordon, Zeinalzadeh, Ashkan, Wenska, Tom, Loomis, Michael, Nation, James B., Fabre, Tiphaine, Tiirikainen, Maarit, Hernandez, Brenda, Chan, Owen, Wong, Linda, Kwee, Sandi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Methodology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4966782/
https://www.ncbi.nlm.nih.gov/pubmed/27478503
http://dx.doi.org/10.1186/s13040-016-0103-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4966782/
https://www.ncbi.nlm.nih.gov/pubmed/27478503
http://dx.doi.org/10.1186/s13040-016-0103-7

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