541 related articles for article (PubMed ID: 32689977)
1. Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns.
Osmala M; Lähdesmäki H
BMC Bioinformatics; 2020 Jul; 21(1):317. PubMed ID: 32689977
[TBL] [Abstract][Full Text] [Related]
2. ChromaSig: a probabilistic approach to finding common chromatin signatures in the human genome.
Hon G; Ren B; Wang W
PLoS Comput Biol; 2008 Oct; 4(10):e1000201. PubMed ID: 18927605
[TBL] [Abstract][Full Text] [Related]
3. Opening up the blackbox: an interpretable deep neural network-based classifier for cell-type specific enhancer predictions.
Kim SG; Theera-Ampornpunt N; Fang CH; Harwani M; Grama A; Chaterji S
BMC Syst Biol; 2016 Aug; 10 Suppl 2(Suppl 2):54. PubMed ID: 27490187
[TBL] [Abstract][Full Text] [Related]
4. Enhancer identification in mouse embryonic stem cells using integrative modeling of chromatin and genomic features.
Chen CY; Morris Q; Mitchell JA
BMC Genomics; 2012 Apr; 13():152. PubMed ID: 22537144
[TBL] [Abstract][Full Text] [Related]
5. RFECS: a random-forest based algorithm for enhancer identification from chromatin state.
Rajagopal N; Xie W; Li Y; Wagner U; Wang W; Stamatoyannopoulos J; Ernst J; Kellis M; Ren B
PLoS Comput Biol; 2013; 9(3):e1002968. PubMed ID: 23526891
[TBL] [Abstract][Full Text] [Related]
6. DELTA: A Distal Enhancer Locating Tool Based on AdaBoost Algorithm and Shape Features of Chromatin Modifications.
Lu Y; Qu W; Shan G; Zhang C
PLoS One; 2015; 10(6):e0130622. PubMed ID: 26091399
[TBL] [Abstract][Full Text] [Related]
7. Predicting enhancers in mammalian genomes using supervised hidden Markov models.
Zehnder T; Benner P; Vingron M
BMC Bioinformatics; 2019 Mar; 20(1):157. PubMed ID: 30917778
[TBL] [Abstract][Full Text] [Related]
8. Integrating diverse datasets improves developmental enhancer prediction.
Erwin GD; Oksenberg N; Truty RM; Kostka D; Murphy KK; Ahituv N; Pollard KS; Capra JA
PLoS Comput Biol; 2014 Jun; 10(6):e1003677. PubMed ID: 24967590
[TBL] [Abstract][Full Text] [Related]
9. Clustered ChIP-Seq-defined transcription factor binding sites and histone modifications map distinct classes of regulatory elements.
Rye M; Sætrom P; Håndstad T; Drabløs F
BMC Biol; 2011 Nov; 9():80. PubMed ID: 22115494
[TBL] [Abstract][Full Text] [Related]
10. DEEP: a general computational framework for predicting enhancers.
Kleftogiannis D; Kalnis P; Bajic VB
Nucleic Acids Res; 2015 Jan; 43(1):e6. PubMed ID: 25378307
[TBL] [Abstract][Full Text] [Related]
11. Evaluating Enhancer Function and Transcription.
Field A; Adelman K
Annu Rev Biochem; 2020 Jun; 89():213-234. PubMed ID: 32197056
[TBL] [Abstract][Full Text] [Related]
12. Histone ChIP-Seq identifies differential enhancer usage during chondrogenesis as critical for defining cell-type specificity.
Cheung K; Barter MJ; Falk J; Proctor CJ; Reynard LN; Young DA
FASEB J; 2020 Apr; 34(4):5317-5331. PubMed ID: 32058623
[TBL] [Abstract][Full Text] [Related]
13. Prediction of regulatory elements in mammalian genomes using chromatin signatures.
Won KJ; Chepelev I; Ren B; Wang W
BMC Bioinformatics; 2008 Dec; 9():547. PubMed ID: 19094206
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Taking promoters out of enhancers in sequence based predictions of tissue-specific mammalian enhancers.
Herman-Izycka J; Wlasnowolski M; Wilczynski B
BMC Med Genomics; 2017 May; 10(Suppl 1):34. PubMed ID: 28589862
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Enhancer target prediction: state-of-the-art approaches and future prospects.
Umarov R; Hon CC
Biochem Soc Trans; 2023 Oct; 51(5):1975-1988. PubMed ID: 37830459
[TBL] [Abstract][Full Text] [Related]
18. Enhancer prediction with histone modification marks using a hybrid neural network model.
Lim A; Lim S; Kim S
Methods; 2019 Aug; 166():48-56. PubMed ID: 30905748
[TBL] [Abstract][Full Text] [Related]
19. Chromatin proteomics reveals novel combinatorial histone modification signatures that mark distinct subpopulations of macrophage enhancers.
Soldi M; Mari T; Nicosia L; Musiani D; Sigismondo G; Cuomo A; Pavesi G; Bonaldi T
Nucleic Acids Res; 2017 Dec; 45(21):12195-12213. PubMed ID: 28981749
[TBL] [Abstract][Full Text] [Related]
20. In silico identification of enhancers on the basis of a combination of transcription factor binding motif occurrences.
Fang Y; Wang Y; Zhu Q; Wang J; Li G
Sci Rep; 2016 Sep; 6():32476. PubMed ID: 27582178
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
