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Supplementary document for Deep learning for fast spatially-varying deconvolution - 5581668.pdf

Autor: Yanny, Kyrollos, Monakhova, Kristina, Shuai, Richard, Waller, Laura

Supplemental Document 1

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2784293348aefa6527dbd0e32cb88dc4

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Current genomic deep learning models display decreased performance in cell type specific accessible regions.

Autor: Kathail P; Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA., Shuai RW; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA., Chung R; Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA., Ye CJ; Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.; Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA.; Chan Zuckerberg Biohub, San Francisco, CA, USA., Loeb GB; Division of Nephrology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.; Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA, USA., Ioannidis NM; Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA.; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA.; Chan Zuckerberg Biohub, San Francisco, CA, USA.

Publikováno v: BioRxiv : the preprint server for biology [bioRxiv] 2024 Jul 10. Date of Electronic Publication: 2024 Jul 10.