A method to predict the impact of regulatory variants from DNA sequence. - Test2 - elhamkhani - TriplyDB

A method to predict the impact of regulatory variants from DNA sequence.

A method to predict the impact of regulatory variants from DNA sequence.

http://www.wikidata.org/entity/Q35904489

about

Novel bioinformatic developments for exome sequencing Prediction of Causal Candidate Genes in Coronary Artery Disease Loci The genetic heterogeneity of colorectal cancer predisposition - guidelines for gene discovery Epigenomic landscapes of retinal rods and cones Genome-wide Association Study of Platelet Count Identifies Ancestry-Specific Loci in Hispanic/Latino Americans Global inference of disease-causing single nucleotide variants from exome sequencing data Quantifying 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 Variants WSMD: 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 Models Which 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 SVM PredictSNP2: 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 Traits Functional genomics bridges the gap between quantitative genetics and molecular biology.Imputation for transcription factor binding predictions based on deep learning A 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 vivo Handling 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 cells Insight into GATA1 transcriptional activity through interrogation of cis elements disrupted in human erythroid disorders DanQ: 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 networks In silico identification of enhancers on the basis of a combination of transcription factor binding motif occurrences A 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 networks Alignment-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-86A6-93C9903C4AB7 Q27342192-5E027E60-CCC7-4866-B66F-49FBDABF8F69 Q28077234-F135F84D-865A-4953-9499-484F8A30A082 Q28550899-54F5E02B-E101-411F-80D7-0CAF7CE79114 Q30277682-8285A17C-3997-4554-92D6-7C68618E6E11 Q31159221-4DD37C67-015D-475D-ACD3-23B16492BA01 Q33557469-5539BCCD-D55A-4B4C-B2F1-E7FB3FB291E1 Q33675161-C750999D-6F51-400C-B2FF-854761F15449 Q33774125-A7CD67DB-39E8-4DB8-94CD-5120931CB4E6 Q33789914-01F107E2-0CDD-4701-A7E4-789F3936CA98 Q33878483-75AB8654-C3F0-449A-AAD1-999712D84953 Q33878882-E0425DC0-96AD-425A-8F8F-F332EDF366B3 Q34518645-993284D4-B5F2-4DE5-A44A-8E878090EF82 Q35806459-C13CCE1C-FB21-4B6C-BD60-EADAC1679F42 Q35840001-931E9309-4BFD-4DB6-85CB-8840D49FEE02 Q35930706-8453C4D7-9FFE-4E41-B0C4-3C531FC86417 Q35991653-B7F69EE8-F45D-4416-9C0D-8EAF82DCAADF Q36011083-CAE9E4D0-23A3-444E-A787-AA2E931DC27A Q36029474-92C60B42-829A-4447-86E5-A3DFF0AEC03D Q36040307-129CBA41-7BB0-4931-9B23-91FDB1BD7B03 Q36084283-BA8B7D16-3E19-45E0-A023-EC07DBF81EAC Q36289441-214382E9-8CE6-4F07-AF03-2DC62EA539BF Q36329488-092BDB74-E97C-4A85-8305-44E94C9ED739 Q36338647-AE1FE050-0517-4624-9E35-03C7765DD3C3 Q36379689-F4DA01F4-EC4F-46B1-BF20-BD98C6F3BF82 Q36621822-9778510A-BB3B-4BDE-AE9F-962BB748E028 Q36796353-66B3FF1B-A8FD-49B3-BD24-7CF1CDFC1773 Q36831545-2CD62CE3-8CCE-487A-8FE6-4C10D8B2A8A6 Q37021556-C43DB514-E34D-4C4C-9EC7-53CA16C72BB9 Q37076984-16E645E5-746F-4B4D-A82C-FA9D17C83653 Q37223613-1E19959B-A167-4E8D-98E3-41E1C2156A06 Q37231376-FE351D17-1E58-455B-A791-D3AD554B7872 Q37472956-EAB9667C-A0F8-4987-97CA-36F19B79C686 Q37613798-10B26753-8E5A-4D5D-9B3E-6941525BB39B Q37708369-0A21DE9F-726D-41AA-99C8-F112176BD85A Q38601823-E5536FD7-3106-4FB9-B1E3-2E2CA1D67F34 Q38695928-E77771FF-554F-4C5F-94D0-78FDD5A35832 Q38755095-BDD9857B-29FD-4D6B-A9AD-B8A2C4A2EDCA Q38784596-5D916C64-0A64-4381-91C9-323E0E49EB34 Q38893481-86016CAB-26A3-4C00-9A91-0CAD702E52A3

P2860

A method to predict the impact of regulatory variants from DNA sequence.

http://www.wikidata.org/entity/Q35904489

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

P1433

Q35904489-73CAABFE-A1DE-42CC-AD04-ADC0D4A06B58

P1476

Q35904489-F0120B58-12ED-4436-B520-121626CEEAA1

P2093

Q35904489-1889F23B-3736-4C3A-8E5D-8B8622327E26

Q35904489-4E62575E-27C3-4B11-AB36-36705D818E16

Q35904489-B4F89A3B-EEF1-4C70-8F76-969A80907E72

Q35904489-B8B7C5A4-7801-4F63-99D1-9C83588BB005

Q35904489-E3B8996F-F16C-4059-B4A5-592022E4D7CF

P2860

Q35904489-03868519-4AFA-4832-BDCD-635D3247CC16

Q35904489-1781D42C-CFDA-491F-8600-0B685A17CF68

Q35904489-1A3F5D85-6236-4193-985B-25F7F6C969A7

Q35904489-2374FCAF-84D8-4205-9735-920509EEF0E4

Q35904489-27971943-ECDC-4F28-904B-758DAC771BA7

Q35904489-2D025A55-D6C2-433D-A40A-1E8EB99DA7B8

Q35904489-2DB673F7-10D0-427D-B57F-3427C073B5C1

Q35904489-31493346-C31C-4025-A70D-662FA3F3551C

Q35904489-32DE00DC-879D-4CC5-9CAF-798303732DF7

Q35904489-33E0D692-F979-4AE9-B16E-BF353BF2FD11

P2888

Q35904489-A921363E-9BC2-4433-AA01-E4A101A87391

P304

Q35904489-99810D6A-1491-4650-8F74-DD53B756F0A4

P31

Q35904489-D6923E32-97C6-4BD4-83C3-582678F06A68

P356

Q35904489-31193536-2A35-43F3-9DC1-5EBE3B24DCD2

P407

Q35904489-9DCE50AE-D939-4C3F-8FE8-E1F58A0C1419

P433

Q35904489-B095D900-8127-4369-909A-13330A14A654

P478

Q35904489-F5EA2663-60B7-4D3E-955A-B05BE697D35D

P50

Q35904489-3D4FFB2E-400C-4043-AAE9-77A90EAA286A

Q35904489-8FDE5FCE-B12A-40BB-A40E-DD97EA1F7226

P577

Q35904489-222AFCEC-9CC1-44F6-940F-B263A00CE304

P5875

Q35904489-625A9B43-7AE9-4A99-898E-F5AAD3829B8E

P698

Q35904489-FF73C9D3-9A52-4D69-A2E6-278838102966

P932

Q35904489-BBED0076-EB87-4A80-A906-D8DD99BBEE50

P356

P1433

Nature Genetics

P1476

A method to predict the impact of regulatory variants from DNA sequence.

@en

P2093

Alessandro L Asoni

http://www.w3.org/2001/XMLSchema#string

Andrew S McCallion

http://www.w3.org/2001/XMLSchema#string

Benjamin J Strober

http://www.w3.org/2001/XMLSchema#string

Maggie Baker

http://www.w3.org/2001/XMLSchema#string

Michael A Beer

http://www.w3.org/2001/XMLSchema#string

P2860

Model-based analysis of ChIP-Seq (MACS)

Potential etiologic and functional implications of genome-wide association loci for human diseases and traits

An integrated encyclopedia of DNA elements in the human genome

Genome-wide association defines more than 30 distinct susceptibility loci for Crohn's disease

Multiple common variants for celiac disease influencing immune gene expression

A map of human genome variation from population-scale sequencing

Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci

Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes

From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus

Dense fine-mapping study identifies new susceptibility loci for primary biliary cirrhosis

P2888

P304

955-961

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.1038/NG.3331

http://www.w3.org/2001/XMLSchema#string

P407

P433

8

http://www.w3.org/2001/XMLSchema#string

P478

47

http://www.w3.org/2001/XMLSchema#string

P50

P577

2015-06-15T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P5875

278330540

http://www.w3.org/2001/XMLSchema#string

P698

26075791

http://www.w3.org/2001/XMLSchema#string

P932

4520745

http://www.w3.org/2001/XMLSchema#string