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Paper Details

Machine Learning Prediction of Non-Coding Variant Impact in Human Retinal cis-Regulatory Elements.
Transl Vis Sci Technol
6
2022
18, Human, Human Retinal cis-Regulatory Elements, TF binding motifs, binding motifs, cis-regulatory elements, cis-regulatory variants, gapped k, human, human retinal CREs, human retinal regulatory elements, negative test sequences, non-, non-coding regulatory sequence variants, pathogenic non-coding sequence variants, putative cis-regulatory elements, reference allele, regulatory variants, reporter, retinal disease, single nucleotide, transcription factor (TF) binding motifs
Humans, Machine Learning, Nucleotides, Phylogeny, Retina, Retinal Diseases, Transcription Factors
Author NameAffiliation
Leah S VandenBoschCenter for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute
Kelsey LuuCenter for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute
Andrew E TimmsCenter for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute
Shriya ChallamCenter for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute
Yue WuUniversity of Washington Department of Ophthalmology
Aaron LeeUniversity of Washington Department of Ophthalmology
Aaron LeeBrotman Baty Institute for Precision Medicine
Timothy J CherryCenter for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute
Timothy J CherryBrotman Baty Institute for Precision Medicine
Timothy J CherryUniversity of Washington Department of Pediatrics
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Release 1.2