775 related articles for article (PubMed ID: 28750606)
1. Assessing the model transferability for prediction of transcription factor binding sites based on chromatin accessibility.
Liu S; Zibetti C; Wan J; Wang G; Blackshaw S; Qian J
BMC Bioinformatics; 2017 Jul; 18(1):355. PubMed ID: 28750606
[TBL] [Abstract][Full Text] [Related]
2. Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast.
Tsai ZT; Shiu SH; Tsai HK
PLoS Comput Biol; 2015 Aug; 11(8):e1004418. PubMed ID: 26291518
[TBL] [Abstract][Full Text] [Related]
3. Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features.
Kumar S; Bucher P
BMC Bioinformatics; 2016 Jan; 17 Suppl 1(Suppl 1):4. PubMed ID: 26818008
[TBL] [Abstract][Full Text] [Related]
4. BinDNase: a discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data.
Kähärä J; Lähdesmäki H
Bioinformatics; 2015 Sep; 31(17):2852-9. PubMed ID: 25957350
[TBL] [Abstract][Full Text] [Related]
5. MixChIP: a probabilistic method for cell type specific protein-DNA binding analysis.
Rautio S; Lähdesmäki H
BMC Bioinformatics; 2015 Dec; 16():413. PubMed ID: 26703974
[TBL] [Abstract][Full Text] [Related]
6. A biophysical model for analysis of transcription factor interaction and binding site arrangement from genome-wide binding data.
He X; Chen CC; Hong F; Fang F; Sinha S; Ng HH; Zhong S
PLoS One; 2009 Dec; 4(12):e8155. PubMed ID: 19956545
[TBL] [Abstract][Full Text] [Related]
7. Mocap: large-scale inference of transcription factor binding sites from chromatin accessibility.
Chen X; Yu B; Carriero N; Silva C; Bonneau R
Nucleic Acids Res; 2017 May; 45(8):4315-4329. PubMed ID: 28334916
[TBL] [Abstract][Full Text] [Related]
8. Nonconsensus Protein Binding to Repetitive DNA Sequence Elements Significantly Affects Eukaryotic Genomes.
Afek A; Cohen H; Barber-Zucker S; Gordân R; Lukatsky DB
PLoS Comput Biol; 2015 Aug; 11(8):e1004429. PubMed ID: 26285121
[TBL] [Abstract][Full Text] [Related]
9. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.
Schmidt F; Gasparoni N; Gasparoni G; Gianmoena K; Cadenas C; Polansky JK; Ebert P; Nordström K; Barann M; Sinha A; Fröhler S; Xiong J; Dehghani Amirabad A; Behjati Ardakani F; Hutter B; Zipprich G; Felder B; Eils J; Brors B; Chen W; Hengstler JG; Hamann A; Lengauer T; Rosenstiel P; Walter J; Schulz MH
Nucleic Acids Res; 2017 Jan; 45(1):54-66. PubMed ID: 27899623
[TBL] [Abstract][Full Text] [Related]
10. Predicting transcription factor binding using ensemble random forest models.
Behjati Ardakani F; Schmidt F; Schulz MH
F1000Res; 2018; 7():1603. PubMed ID: 31723409
[No Abstract] [Full Text] [Related]
11. Integrative prediction of gene expression with chromatin accessibility and conformation data.
Schmidt F; Kern F; Schulz MH
Epigenetics Chromatin; 2020 Feb; 13(1):4. PubMed ID: 32029002
[TBL] [Abstract][Full Text] [Related]
12. Anchor: trans-cell type prediction of transcription factor binding sites.
Li H; Quang D; Guan Y
Genome Res; 2019 Feb; 29(2):281-292. PubMed ID: 30567711
[TBL] [Abstract][Full Text] [Related]
13. Cell-type specificity of ChIP-predicted transcription factor binding sites.
Håndstad T; Rye M; Močnik R; Drabløs F; Sætrom P
BMC Genomics; 2012 Aug; 13():372. PubMed ID: 22863112
[TBL] [Abstract][Full Text] [Related]
14. Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome.
Karimzadeh M; Hoffman MM
Genome Biol; 2022 Jun; 23(1):126. PubMed ID: 35681170
[TBL] [Abstract][Full Text] [Related]
15. High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions.
Agius P; Arvey A; Chang W; Noble WS; Leslie C
PLoS Comput Biol; 2010 Sep; 6(9):. PubMed ID: 20838582
[TBL] [Abstract][Full Text] [Related]
16. Transcription factor-binding k-mer analysis clarifies the cell type dependency of binding specificities and cis-regulatory SNPs in humans.
Tahara S; Tsuchiya T; Matsumoto H; Ozaki H
BMC Genomics; 2023 Oct; 24(1):597. PubMed ID: 37805453
[TBL] [Abstract][Full Text] [Related]
17. An improved ChIP-seq peak detection system for simultaneously identifying post-translational modified transcription factors by combinatorial fusion, using SUMOylation as an example.
Cheng CY; Chu CH; Hsu HW; Hsu FR; Tang CY; Wang WC; Kung HJ; Chang PC
BMC Genomics; 2014; 15 Suppl 1(Suppl 1):S1. PubMed ID: 24564277
[TBL] [Abstract][Full Text] [Related]
18. Base-resolution methylation patterns accurately predict transcription factor bindings in vivo.
Xu T; Li B; Zhao M; Szulwach KE; Street RC; Lin L; Yao B; Zhang F; Jin P; Wu H; Qin ZS
Nucleic Acids Res; 2015 Mar; 43(5):2757-66. PubMed ID: 25722376
[TBL] [Abstract][Full Text] [Related]
19. Computational modeling of chromatin accessibility identified important epigenomic regulators.
Zhao Y; Dong Y; Hong W; Jiang C; Yao K; Cheng C
BMC Genomics; 2022 Jan; 23(1):19. PubMed ID: 34996354
[TBL] [Abstract][Full Text] [Related]
20. Modeling co-occupancy of transcription factors using chromatin features.
Liu L; Zhao W; Zhou X
Nucleic Acids Res; 2016 Mar; 44(5):e49. PubMed ID: 26590261
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]