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Asymmetric trichotomous partitioning overcomes dataset limitations in building machine learning models for predicting siRNA efficacy

Molecular Therapy — Nucleic Acids(2023)10.1016/j.omtn.2023.06.010Source: DataRank Database

Asymmetric trichotomous partitioning overcomes dataset limitations in building machine learning models for predicting siRNA efficacy is a research paper published in Molecular Therapy — Nucleic Acids (2023). On theSindex it has a DataRank of 0. It has been cited 25 times.

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Open Access25 citations

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refseq

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (2 grants)OA: goldTopics (11)IDs (PubMed, PMC)Annotations (1)

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Accessible (1/2)

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Reusable (0/3)

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Authors (3)

Dmitry Korkin ORCID,Anastasia Khvorova ORCID,Kathryn R. Monopoli

Live enrichment

PubMed:37456778 PMC:PMC10338369

National Institutes of Health

Grant: R35 GM131839

National Institutes of Health

Grant: S10 OD020012

goldLicense: cc-by

publisherhttp://www.cell.com/article/S2162253123001609/pdf

publisherhttps://api.elsevier.com/content/article/PII:S2162253123001609?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S2162253123001609?httpAccept=text/plain

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC10338369/pdf/main.pdf

repositoryhttps://doaj.org/article/53d9349f29c8424096fc0c8893af6f24

Keywords

OligonucleotidesArtificial intelligenceRNA interferenceSiRNAMachine LearningRandom ForestChemical ModificationsSupervised LearningOligonucleotide TherapeuticsMt: Oligonucleotides: Therapies And ApplicationsComputational Model Development