Integration of ChIP-seq and machine learning reveals enhancers and a predictive regulatory sequence vocabulary in melanocytes.
about
Analysis of Genomic Sequence Motifs for Deciphering Transcription Factor Binding and Transcriptional Regulation in Eukaryotic CellsA polymorphism in IRF4 affects human pigmentation through a tyrosinase-dependent MITF/TFAP2A pathwayEpigenomic landscapes of retinal rods and conesEP-DNN: A Deep Neural Network-Based Global Enhancer Prediction Algorithmkmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data setsHigh-throughput functional testing of ENCODE segmentation predictions.Topology of mammalian developmental enhancers and their regulatory landscapes.Identifying causal regulatory SNPs in ChIP-seq enhancersEnhancer modeling uncovers transcriptional signatures of individual cardiac cell states in Drosophila.Integrating diverse datasets improves developmental enhancer predictionEnhanced regulatory sequence prediction using gapped k-mer features.Occupancy by key transcription factors is a more accurate predictor of enhancer activity than histone modifications or chromatin accessibility.Human Enhancers Are Fragile and Prone to Deactivating MutationsFunctional validation of mouse tyrosinase non-coding regulatory DNA elements by CRISPR-Cas9-mediated mutagenesisIdentification of Predictive Cis-Regulatory Elements Using a Discriminative Objective Function and a Dynamic Search Space.Genetics of skin color variation in Europeans: genome-wide association studies with functional follow-up.Identification of High-Impact cis-Regulatory Mutations Using Transcription Factor Specific Random Forest ModelsA method to predict the impact of regulatory variants from DNA sequence.gkmSVM: an R package for gapped-kmer SVMGenomic analysis reveals distinct mechanisms and functional classes of SOX10-regulated genes in melanocytes.In Silico Analysis of Gene Expression Network Components Underlying Pigmentation Phenotypes in the Python Identified Evolutionarily Conserved Clusters of Transcription Factor Binding SitesTFAP2 paralogs regulate melanocyte differentiation in parallel with MITF.Discovery of cell-type specific regulatory elements in the human genome using differential chromatin modification analysis.Sequence signatures extracted from proximal promoters can be used to predict distal enhancersComputational schemes for the prediction and annotation of enhancers from epigenomic assays.Human skin color is influenced by an intergenic DNA polymorphism regulating transcription of the nearby BNC2 pigmentation gene.Predicting enhancer activity and variant impact using gkm-SVM.Enhancer-targeted genome editing selectively blocks innate resistance to oncokinase inhibition.Optimizing ChIP-seq peak detectors using visual labels and supervised machine learning.Beyond MITF: Multiple transcription factors directly regulate the cellular phenotype in melanocytes and melanoma.LS-GKM: a new gkm-SVM for large-scale datasetsRole of HGF-MET Signaling in Primary and Acquired Resistance to Targeted Therapies in Cancer.Axing the cancer loop.Human cardiac -regulatory elements, their cognate transcription factors, and regulatory DNA sequence variants
P2860
Q26768509-3267E69F-DB3E-46E6-80DE-5947E87F3D40Q28302540-F1F2AFE4-19E7-4E34-AD7B-811B89DA0232Q28550899-FE3DC29F-6F4B-4D4C-8B87-763A86358AB8Q30367835-1D746634-2614-4E2C-9484-51E888555EB6Q30648830-B3A531A5-37D0-43A1-9FE4-65B536D0C110Q34350068-D91C96C2-3CA1-42FF-BFC0-E5E4FEAA7564Q34379841-F664AC19-B369-43AA-8DDC-FC3ED0537B1FQ34883568-764421A5-3D0E-4406-B052-AF9C3672B6E3Q35089032-1F28F153-1C7F-4D4A-B268-7F5125783CC8Q35195359-DA30E2F6-017A-41D4-B6FB-021F37CC6F7FQ35208332-9E28B6D3-5CA8-4C31-9680-CFBA289F1439Q35608650-06813BE1-4C08-4D5F-9EFC-D995D3698A82Q35632318-28AFB5A4-D798-46D9-92DE-614D567DB021Q35656786-3D4FD747-C200-4D3F-A7AB-A6AE3C05623DQ35806459-77883C83-B477-4DD6-BDDA-9D60317FF1C8Q35829596-3AD783AD-F499-43F8-B99E-E39221D7116CQ35840001-62E07E71-FFCA-4334-9C3C-8AB9E04139FEQ35904489-E5DE8123-C392-48B6-938E-306905202FDCQ36011083-BD18449E-EF4C-4B56-BE14-D124EAEEE043Q36065451-47F58D3F-64D6-4F66-95E6-37E04DD62C89Q36152226-E6AE3906-FF7C-422B-95CC-300A9DA40C15Q36294018-F0A59D58-54E9-4456-A2FF-15ACD1D1958CQ37271106-901FEAA7-8C15-42AD-A4D3-FC32469C30D6Q37697916-0758F1CA-DB64-42A9-8CCC-AA36688A7BA4Q38280813-E72C01E2-FD4D-4B11-A27E-D9E16D32F17EQ38986048-92434606-1F60-4D84-98FE-540BC8BD7F59Q39000601-D2F5EEE4-0135-4DBA-BBC1-1292D415B8D3Q39032842-6BC9BBE2-A01D-463C-9ABC-9F2FAE8858E2Q39232183-71E5AB45-7800-49BE-8CA0-3190D2D085D9Q39397994-8F7CA2EC-A6FF-4D07-9B9A-A6E435B59BB9Q39788490-CEC50B5F-D8B7-445F-9833-F31E9D93B42DQ53711253-40856A6E-D895-4B95-AAF7-F22F64C08DDFQ55073580-9B4F77EE-384E-47C7-B345-375A1F0B46C1Q58792891-FC78D79D-763E-446D-84E0-7A59CE1E8E97
P2860
Integration of ChIP-seq and machine learning reveals enhancers and a predictive regulatory sequence vocabulary in melanocytes.
description
2012 nî lūn-bûn
@nan
2012年の論文
@ja
2012年学术文章
@wuu
2012年学术文章
@zh-cn
2012年学术文章
@zh-hans
2012年学术文章
@zh-my
2012年学术文章
@zh-sg
2012年學術文章
@yue
2012年學術文章
@zh
2012年學術文章
@zh-hant
name
Integration of ChIP-seq and ma ...... nce vocabulary in melanocytes.
@ast
Integration of ChIP-seq and ma ...... nce vocabulary in melanocytes.
@en
type
label
Integration of ChIP-seq and ma ...... nce vocabulary in melanocytes.
@ast
Integration of ChIP-seq and ma ...... nce vocabulary in melanocytes.
@en
prefLabel
Integration of ChIP-seq and ma ...... nce vocabulary in melanocytes.
@ast
Integration of ChIP-seq and ma ...... nce vocabulary in melanocytes.
@en
P2093
P2860
P50
P356
P1433
P1476
Integration of ChIP-seq and ma ...... nce vocabulary in melanocytes.
@en
P2093
Andrew S McCallion
Christopher Fletez-Brant
Michael A Beer
Seneca L Bessling
Xylena Reed
P2860
P304
P356
10.1101/GR.139360.112
P577
2012-09-27T00:00:00Z