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Applying machine learning to predict viral assembly for adeno-associated virus capsid libraries

Molecular Therapy — Methods & Clinical Development(2020)10.1016/j.omtm.2020.11.017Source: DataRank Database

Applying machine learning to predict viral assembly for adeno-associated virus capsid libraries is a research paper published in Molecular Therapy — Methods & Clinical Development (2020). On theSindex it has a DataRank of 0. It has been cited 52 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access52 citations

Cite:

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (2 grants)OA: goldTopics (16)IDs (PubMed)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (6)

Michael P. Kummer ORCID,Oleksandr Kondratov ORCID,Arunava Banerjee ORCID,Oleksandr Moskalenko,Sergei Zolotukhin

Live enrichment

PubMed:33511242

National Institutes of Health

Grant: 5R01EY024280-07

Engineering AAV for safe and efficient gene delivery to the human retina

NEI NIH HHS

Grant: R01 EY024280

goldLicense: cc-by-nc-nd

publisherhttp://www.cell.com/article/S2329050120302448/pdf

publisherhttps://api.elsevier.com/content/article/PII:S2329050120302448?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S2329050120302448?httpAccept=text/plain

repositoryhttps://doaj.org/article/b2eb5e6ad8804298a15cddd438452aff

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/7809249

Fields of Study

0301 basic medicine0206 medical engineering02 engineering and technology03 medical and health sciences

Keywords

QH573-671AssemblyAAVQH426-470Machine LearningCapsid LibrariesPackagingGeneticsOriginal ArticleANNCytologySVM

Sustainable Development Goals

SDG 3: 3. Good health