A semisupervised model to predict regulatory effects of genetic variants at single nucleotide resolution using massively parallel reporter assays | Zendy

Zikun Yang | Zendy; Chen Wang | Zendy; S. Erjavec | Zendy; Lynn Petukhova | Zendy; Angela M. Christiano | Zendy; Iuliana IonitaLaza | Zendy

Open Access

A semisupervised model to predict regulatory effects of genetic variants at single nucleotide resolution using massively parallel reporter assays

Author(s) -

Zikun Yang,

Chen Wang,

S. Erjavec,

Lynn Petukhova,

Angela M. Christiano,

Iuliana IonitaLaza

Publication year - 2021

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btab040

Subject(s) - computational biology , biology , single nucleotide polymorphism , regulatory sequence , genomics , functional genomics , context (archaeology) , genome wide association study , genetics , computer science , gene , regulation of gene expression , genome , paleontology , genotype

Predicting regulatory effects of genetic variants is a challenging but important problem in functional genomics. Given the relatively low sensitivity of functional assays, and the pervasiveness of class imbalance in functional genomic data, popular statistical prediction models can sharply underestimate the probability of a regulatory effect. We describe here the presence-only model (PO-EN), a type of semi-supervised model, to predict regulatory effects of genetic variants at sequence-level resolution in a context of interest by integrating a large number of epigenetic features and massively parallel reporter assays (MPRAs).

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