193 related articles for article (PubMed ID: 31510653)
1. Selfish: discovery of differential chromatin interactions via a self-similarity measure.
Ardakany AR; Ay F; Lonardi S
Bioinformatics; 2019 Jul; 35(14):i145-i153. PubMed ID: 31510653
[TBL] [Abstract][Full Text] [Related]
2. Improving comparative analyses of Hi-C data via contrastive self-supervised learning.
Li H; He X; Kurowski L; Zhang R; Zhao D; Zeng J
Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37287135
[TBL] [Abstract][Full Text] [Related]
3. GenomeDISCO: a concordance score for chromosome conformation capture experiments using random walks on contact map graphs.
Ursu O; Boley N; Taranova M; Wang YXR; Yardimci GG; Stafford Noble W; Kundaje A
Bioinformatics; 2018 Aug; 34(16):2701-2707. PubMed ID: 29554289
[TBL] [Abstract][Full Text] [Related]
4. Assessing stationary distributions derived from chromatin contact maps.
Segal MR; Fletez-Brant K
BMC Bioinformatics; 2020 Feb; 21(1):73. PubMed ID: 32093610
[TBL] [Abstract][Full Text] [Related]
5. Identification and utilization of copy number information for correcting Hi-C contact map of cancer cell lines.
Khalil AIS; Muzaki SRBM; Chattopadhyay A; Sanyal A
BMC Bioinformatics; 2020 Nov; 21(1):506. PubMed ID: 33160308
[TBL] [Abstract][Full Text] [Related]
6. A global high-density chromatin interaction network reveals functional long-range and trans-chromosomal relationships.
Lohia R; Fox N; Gillis J
Genome Biol; 2022 Nov; 23(1):238. PubMed ID: 36352464
[TBL] [Abstract][Full Text] [Related]
7. Identification of copy number variations and translocations in cancer cells from Hi-C data.
Chakraborty A; Ay F
Bioinformatics; 2018 Jan; 34(2):338-345. PubMed ID: 29048467
[TBL] [Abstract][Full Text] [Related]
8. multiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments.
Stansfield JC; Cresswell KG; Dozmorov MG
Bioinformatics; 2019 Sep; 35(17):2916-2923. PubMed ID: 30668639
[TBL] [Abstract][Full Text] [Related]
9. FreeHi-C spike-in simulations for benchmarking differential chromatin interaction detection.
Zheng Y; Zhou P; Keleş S
Methods; 2021 May; 189():3-11. PubMed ID: 32663510
[TBL] [Abstract][Full Text] [Related]
10. EnHiC: learning fine-resolution Hi-C contact maps using a generative adversarial framework.
Hu Y; Ma W
Bioinformatics; 2021 Jul; 37(Suppl_1):i272-i279. PubMed ID: 34252966
[TBL] [Abstract][Full Text] [Related]
11. MAPS: Model-based analysis of long-range chromatin interactions from PLAC-seq and HiChIP experiments.
Juric I; Yu M; Abnousi A; Raviram R; Fang R; Zhao Y; Zhang Y; Qiu Y; Yang Y; Li Y; Ren B; Hu M
PLoS Comput Biol; 2019 Apr; 15(4):e1006982. PubMed ID: 30986246
[TBL] [Abstract][Full Text] [Related]
12. DFHiC: a dilated full convolution model to enhance the resolution of Hi-C data.
Wang B; Liu K; Li Y; Wang J
Bioinformatics; 2023 May; 39(5):. PubMed ID: 37084258
[TBL] [Abstract][Full Text] [Related]
13. Efficient Hi-C inversion facilitates chromatin folding mechanism discovery and structure prediction.
Schuette G; Ding X; Zhang B
Biophys J; 2023 Sep; 122(17):3425-3438. PubMed ID: 37496267
[TBL] [Abstract][Full Text] [Related]
14. Practical Analysis of Genome Contact Interaction Experiments.
Carty MA; Elemento O
Methods Mol Biol; 2016; 1418():177-89. PubMed ID: 27008015
[TBL] [Abstract][Full Text] [Related]
15. BART3D: inferring transcriptional regulators associated with differential chromatin interactions from Hi-C data.
Wang Z; Zhang Y; Zang C
Bioinformatics; 2021 Sep; 37(18):3075-3078. PubMed ID: 33720325
[TBL] [Abstract][Full Text] [Related]
16. HiCARN: resolution enhancement of Hi-C data using cascading residual networks.
Hicks P; Oluwadare O
Bioinformatics; 2022 Apr; 38(9):2414-2421. PubMed ID: 35274679
[TBL] [Abstract][Full Text] [Related]
17. HiCcompare: an R-package for joint normalization and comparison of HI-C datasets.
Stansfield JC; Cresswell KG; Vladimirov VI; Dozmorov MG
BMC Bioinformatics; 2018 Jul; 19(1):279. PubMed ID: 30064362
[TBL] [Abstract][Full Text] [Related]
18. Detecting Spatial Chromatin Organization by Chromosome Conformation Capture II: Genome-Wide Profiling by Hi-C.
Vietri Rudan M; Hadjur S; Sexton T
Methods Mol Biol; 2017; 1589():47-74. PubMed ID: 26900130
[TBL] [Abstract][Full Text] [Related]
19. Benchmark of software tools for prokaryotic chromosomal interaction domain identification.
Magnitov MD; Kuznetsova VS; Ulianov SV; Razin SV; Tyakht AV
Bioinformatics; 2020 Nov; 36(17):4560-4567. PubMed ID: 32492116
[TBL] [Abstract][Full Text] [Related]
20. Be-1DCNN: a neural network model for chromatin loop prediction based on bagging ensemble learning.
Wu H; Zhou B; Zhou H; Zhang P; Wang M
Brief Funct Genomics; 2023 Nov; 22(5):475-484. PubMed ID: 37133976
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]