Computational Elucidation of the Regulatory SNPs in the Non-Coding Regions of the Human Genome
Y Yao - 2021 - ir.library.oregonstate.edu
We describe a series of novel computational models, CERENKOV (Computational
Elucidation of the REgulatory NonKOding Variome) and its successors CERENKOV2 …
Elucidation of the REgulatory NonKOding Variome) and its successors CERENKOV2 …
Cerenkov: Computational elucidation of the regulatory noncoding variome
We describe a novel computational approach, CERENKOV (Computational Elucidation of
the REgulatory NonKOd-ing Variome), for discriminating regulatory single nucleotide …
the REgulatory NonKOd-ing Variome), for discriminating regulatory single nucleotide …
[HTML][HTML] CERENKOV2: improved detection of functional noncoding SNPs using data-space geometric features
Background We previously reported on CERENKOV, an approach for identifying regulatory
single nucleotide polymorphisms (rSNPs) that is based on 246 annotation features …
single nucleotide polymorphisms (rSNPs) that is based on 246 annotation features …
CERENKOV3: Clustering and molecular network-derived features improve computational prediction of functional noncoding SNPs
Y Yao, SA Ramsey - PACIFIC SYMPOSIUM ON BIOCOMPUTING …, 2019 - World Scientific
Identification of causal noncoding single nucleotide polymorphisms (SNPs) is important for
maximizing the knowledge dividend from human genome-wide association studies (GWAS) …
maximizing the knowledge dividend from human genome-wide association studies (GWAS) …
Combining eQTL and SNP annotation data to identify functional noncoding SNPs in GWAS trait-associated regions
SA Ramsey, Z Liu, Y Yao, B Weeder - eQTL Analysis: Methods and …, 2020 - Springer
We describe a statistical method for prioritizing candidate causal noncoding single
nucleotide polymorphisms (SNPs) in regions of the genome that are detected as trait …
nucleotide polymorphisms (SNPs) in regions of the genome that are detected as trait …
Predicting functional regulatory polymorphisms
A Torkamani, NJ Schork - Bioinformatics, 2008 - academic.oup.com
Motivation: Limited availability of data has hindered the development of algorithms that can
identify functionally meaningful regulatory single nucleotide polymorphisms (rSNPs). Given …
identify functionally meaningful regulatory single nucleotide polymorphisms (rSNPs). Given …
GSCNN: A genomic selection convolutional neural network model based on SNP genotype and physical distance features and data augmentation strategy
L Ji, W Hou, L Xiong, H Zhou, C Liu, L Li, Z Yuan - 2024 - researchsquare.com
Background Genomic selection (GS) proves to be an effective method for augmenting plant
and animal breeding efficiency. Deep learning displays remarkable flexibility and vast …
and animal breeding efficiency. Deep learning displays remarkable flexibility and vast …
Mining the unknown: assigning function to noncoding single nucleotide polymorphisms
SS Nishizaki, AP Boyle - Trends in Genetics, 2017 - cell.com
One of the formative goals of genetics research is to understand how genetic variation leads
to phenotypic differences and human disease. Genome-wide association studies (GWASs) …
to phenotypic differences and human disease. Genome-wide association studies (GWASs) …
A computational method for prediction of rSNPs in human genome
Regulatory single nucleotide polymorphisms (rSNPs) in human genomes are thought to be
responsible for phenotypic differences, including susceptibility to diseases and treatment …
responsible for phenotypic differences, including susceptibility to diseases and treatment …
TAGOOS: genome-wide supervised learning of non-coding loci associated to complex phenotypes
A González, M Artufel, P Rihet - Nucleic Acids Research, 2019 - academic.oup.com
Genome-wide association studies (GWAS) associate single nucleotide polymorphisms
(SNPs) to complex phenotypes. Most human SNPs fall in non-coding regions and are likely …
(SNPs) to complex phenotypes. Most human SNPs fall in non-coding regions and are likely …