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Using Deep Learning to Extrapolate Protein Expression Measurements

PROTEOMICS(2020)10.1002/pmic.202000009Source: DataRank Database

Using Deep Learning to Extrapolate Protein Expression Measurements is a research paper published in PROTEOMICS (2020). On theSindex it has a DataRank of 0. It has been cited 14 times.

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Open Access14 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (6 grants)OA: hybridTopics (14)IDs (PubMed)

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Reusable (0/3)

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Authors (15)

Kārlis Freivalds ORCID,James C. Wright ORCID,Mārtiņš Opmanis ORCID,Dārta Rituma,Fatemeh Zamanzad Ghavidel ORCID

Live enrichment

PubMed:23304386

Wellcome Trust

Grant: Grant number 208391/Z/17/Z

Latvijas Zinātnes Padome

Grant: Grant number 2019/1‐0432

European Regional Development Fund

Grant: ERDF project 1.1.1.1/16/A/135

Wellcome Trust

Grant: unidentified

unidentified

National Institutes of Health

Grant: 5U41HG007234-08

GENCODE: comprehensive genome annotation for human and mouse

Wellcome Trust

Grant: 208391

The PRIDE database: A proteomics data hub in the life sciences

hybridLicense: cc-by

publisherhttps://analyticalsciencejournals.onlinelibrary.wiley.com/doi/pdfdirect/10.1002/pmic.202000009

publisherhttps://onlinelibrary.wiley.com/doi/pdf/10.1002/pmic.202000009

publisherhttps://onlinelibrary.wiley.com/doi/full-xml/10.1002/pmic.202000009

publisherhttps://analyticalsciencejournals.onlinelibrary.wiley.com/doi/pdf/10.1002/pmic.202000009

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/7757209

Fields of Study

0301 basic medicine0303 health sciences03 medical and health sciences

Keywords

Proteomics570610ProteinsMolecular Sequence Annotation612Mass SpectrometryMiceDeep LearningAnimalsResearch Articles