A method to predict the impact of regulatory variants from DNA sequence.
about
Novel bioinformatic developments for exome sequencingPrediction of Causal Candidate Genes in Coronary Artery Disease LociThe genetic heterogeneity of colorectal cancer predisposition - guidelines for gene discoveryEpigenomic landscapes of retinal rods and conesGenome-wide Association Study of Platelet Count Identifies Ancestry-Specific Loci in Hispanic/Latino AmericansGlobal inference of disease-causing single nucleotide variants from exome sequencing dataQuantifying deleterious effects of regulatory variants.Genome-wide association study of red blood cell traits in Hispanics/Latinos: The Hispanic Community Health Study/Study of Latinos.Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding VariantsWSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data.Non-Coding Loss-of-Function Variation in Human Genomes.Predicting the impact of non-coding variants on DNA methylation.Variation Interpretation Predictors: Principles, Types, Performance, and Choice.Identification of Predictive Cis-Regulatory Elements Using a Discriminative Objective Function and a Dynamic Search Space.Identification of High-Impact cis-Regulatory Mutations Using Transcription Factor Specific Random Forest ModelsWhich Genetics Variants in DNase-Seq Footprints Are More Likely to Alter Binding?Software Application Profile: RVPedigree: a suite of family-based rare variant association tests for normally and non-normally distributed quantitative traits.gkmSVM: an R package for gapped-kmer SVMPredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions.Systematic Functional Dissection of Common Genetic Variation Affecting Red Blood Cell TraitsFunctional genomics bridges the gap between quantitative genetics and molecular biology.Imputation for transcription factor binding predictions based on deep learningA sequence-based method to predict the impact of regulatory variants using random forest.Large-scale identification of sequence variants influencing human transcription factor occupancy in vivoHandling High-Dimension (High-Feature) MicroRNA Data.Predicting effects of noncoding variants with deep learning-based sequence model.DNA context represents transcription regulation of the gene in mouse embryonic stem cellsInsight into GATA1 transcriptional activity through interrogation of cis elements disrupted in human erythroid disordersDanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.Basset: learning the regulatory code of the accessible genome with deep convolutional neural networksIn silico identification of enhancers on the basis of a combination of transcription factor binding motif occurrencesA Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease.Evaluating the impact of single nucleotide variants on transcription factor binding.Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps.cepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes.Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networksAlignment-free inference of hierarchical and reticulate phylogenomic relationships.Predicting gene expression in massively parallel reporter assays: A comparative study.Mining the Unknown: Assigning Function to Noncoding Single Nucleotide Polymorphisms.Identification and Function of Enhancers in the Human Genome.
P2860
Q24658618-1B257C2C-4F4C-452B-86A6-93C9903C4AB7Q27342192-5E027E60-CCC7-4866-B66F-49FBDABF8F69Q28077234-F135F84D-865A-4953-9499-484F8A30A082Q28550899-54F5E02B-E101-411F-80D7-0CAF7CE79114Q30277682-8285A17C-3997-4554-92D6-7C68618E6E11Q31159221-4DD37C67-015D-475D-ACD3-23B16492BA01Q33557469-5539BCCD-D55A-4B4C-B2F1-E7FB3FB291E1Q33675161-C750999D-6F51-400C-B2FF-854761F15449Q33774125-A7CD67DB-39E8-4DB8-94CD-5120931CB4E6Q33789914-01F107E2-0CDD-4701-A7E4-789F3936CA98Q33878483-75AB8654-C3F0-449A-AAD1-999712D84953Q33878882-E0425DC0-96AD-425A-8F8F-F332EDF366B3Q34518645-993284D4-B5F2-4DE5-A44A-8E878090EF82Q35806459-C13CCE1C-FB21-4B6C-BD60-EADAC1679F42Q35840001-931E9309-4BFD-4DB6-85CB-8840D49FEE02Q35930706-8453C4D7-9FFE-4E41-B0C4-3C531FC86417Q35991653-B7F69EE8-F45D-4416-9C0D-8EAF82DCAADFQ36011083-CAE9E4D0-23A3-444E-A787-AA2E931DC27AQ36029474-92C60B42-829A-4447-86E5-A3DFF0AEC03DQ36040307-129CBA41-7BB0-4931-9B23-91FDB1BD7B03Q36084283-BA8B7D16-3E19-45E0-A023-EC07DBF81EACQ36289441-214382E9-8CE6-4F07-AF03-2DC62EA539BFQ36329488-092BDB74-E97C-4A85-8305-44E94C9ED739Q36338647-AE1FE050-0517-4624-9E35-03C7765DD3C3Q36379689-F4DA01F4-EC4F-46B1-BF20-BD98C6F3BF82Q36621822-9778510A-BB3B-4BDE-AE9F-962BB748E028Q36796353-66B3FF1B-A8FD-49B3-BD24-7CF1CDFC1773Q36831545-2CD62CE3-8CCE-487A-8FE6-4C10D8B2A8A6Q37021556-C43DB514-E34D-4C4C-9EC7-53CA16C72BB9Q37076984-16E645E5-746F-4B4D-A82C-FA9D17C83653Q37223613-1E19959B-A167-4E8D-98E3-41E1C2156A06Q37231376-FE351D17-1E58-455B-A791-D3AD554B7872Q37472956-EAB9667C-A0F8-4987-97CA-36F19B79C686Q37613798-10B26753-8E5A-4D5D-9B3E-6941525BB39BQ37708369-0A21DE9F-726D-41AA-99C8-F112176BD85AQ38601823-E5536FD7-3106-4FB9-B1E3-2E2CA1D67F34Q38695928-E77771FF-554F-4C5F-94D0-78FDD5A35832Q38755095-BDD9857B-29FD-4D6B-A9AD-B8A2C4A2EDCAQ38784596-5D916C64-0A64-4381-91C9-323E0E49EB34Q38893481-86016CAB-26A3-4C00-9A91-0CAD702E52A3
P2860
A method to predict the impact of regulatory variants from DNA sequence.
description
2015 nî lūn-bûn
@nan
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
2015年论文
@zh
2015年论文
@zh-cn
name
A method to predict the impact of regulatory variants from DNA sequence.
@ast
A method to predict the impact of regulatory variants from DNA sequence.
@en
type
label
A method to predict the impact of regulatory variants from DNA sequence.
@ast
A method to predict the impact of regulatory variants from DNA sequence.
@en
prefLabel
A method to predict the impact of regulatory variants from DNA sequence.
@ast
A method to predict the impact of regulatory variants from DNA sequence.
@en
P2093
P2860
P356
P1433
P1476
A method to predict the impact of regulatory variants from DNA sequence.
@en
P2093
Alessandro L Asoni
Andrew S McCallion
Benjamin J Strober
Maggie Baker
Michael A Beer
P2860
P2888
P304
P356
10.1038/NG.3331
P407
P577
2015-06-15T00:00:00Z