Profiled support vector machines for antisense oligonucleotide efficacy prediction
Profiled support vector machines for antisense oligonucleotide efficacy prediction
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Publisher
England: BioMed Central Ltd
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Language
English
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England: BioMed Central Ltd
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This paper presents the use of Support Vector Machines (SVMs) for prediction and analysis of antisense oligonucleotide (AO) efficacy. The collected database comprises 315 AO molecules including 68 features each, inducing a problem well-suited to SVMs. The task of feature selection is crucial given the presence of noisy or redundant features, and th...
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Full title
Profiled support vector machines for antisense oligonucleotide efficacy prediction
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TN_cdi_doaj_primary_oai_doaj_org_article_fba21ae83ab9428d945e5caf94933e20
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_fba21ae83ab9428d945e5caf94933e20
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ISSN
1471-2105
E-ISSN
1471-2105
DOI
10.1186/1471-2105-5-135
