Precise uncertain significance prediction using latent space matrix factorization models: genomics variant and heterogeneous clinical data-driven approaches

Sina Abdollahi; Peng-chan Lin; Meng-ru Shen; Jung-hsien Chiang

doi:10.1093/bib/bbaa281

Precise uncertain significance prediction using latent space matrix factorization models: genomics variant and heterogeneous clinical data-driven approaches

Sina Abdollahi, Peng-chan Lin, Meng-ru Shen, Jung-hsien Chiang

Research output: Contribution to journal › Article › peer-review

Original language	English
Journal	Briefings in bioinformatics
DOIs	https://doi.org/10.1093/bib/bbaa281
Publication status	Published - 2020 Nov 14

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10.1093/bib/bbaa281

https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbaa281/5981724

Cite this

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title = "Precise uncertain significance prediction using latent space matrix factorization models: genomics variant and heterogeneous clinical data-driven approaches",

author = "Sina Abdollahi and Peng-chan Lin and Meng-ru Shen and Jung-hsien Chiang",

year = "2020",

month = nov,

day = "14",

doi = "10.1093/bib/bbaa281",

language = "English",

journal = "Briefings in bioinformatics",

issn = "1467-5463",

publisher = "Oxford University Press",

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T1 - Precise uncertain significance prediction using latent space matrix factorization models: genomics variant and heterogeneous clinical data-driven approaches

AU - Abdollahi, Sina

AU - Lin, Peng-chan

AU - Shen, Meng-ru

AU - Chiang, Jung-hsien

PY - 2020/11/14

Y1 - 2020/11/14

U2 - 10.1093/bib/bbaa281

DO - 10.1093/bib/bbaa281

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SN - 1467-5463

JO - Briefings in bioinformatics

JF - Briefings in bioinformatics

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