Author

Jasper Snoek

Google (United States)

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Genomics· 2

Signature Assays, Organisms & Methods

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Assay:DNA methylation· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Research Focus:database / resource· 1 Approach:deep learning· 1 Research Focus:eQTL / molecular QTL mapping· 1 Research Focus:fine-mapping· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Cory Y. McLeaniD· 1 shared David BelangeriD· 1 shared David R Kelley· 1 shared David R. KelleyiD· 1 shared John Rinn· 1 shared Maxwell L. BileschiiD· 1 shared Yakir ReshefiD· 1 shared

Activity by Year

2016-2018 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 11 mutual co-author links

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1 shared paperGenomics

Cory Y. McLeanORCID

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2 papers

Recent·Most cited

Sequential regulatory activity prediction across chromosomes with convolutional neural networks

David R. Kelley, Yakir Reshef, Maxwell L. Bileschi, David Belanger, Cory Y. McLean, Jasper Snoek

The paper develops a machine learning system using convolutional neural networks to predict cell type-specific epigenetic and transcriptional profiles from DNA sequence, showing alignment with eQTL causal variants.

Genome Research · May 1, 2018Peer-reviewedGenomics

Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks.

David R Kelley, Jasper Snoek, John Rinn

Basset uses deep convolutional neural networks to predict DNA sequence functionality and annotate noncoding variants' impact on chromatin accessibility.

bioRxiv · Feb 18, 2016Genomics