191 related articles for article (PubMed ID: 31175825)
1. Learning common and specific patterns from data of multiple interrelated biological scenarios with matrix factorization.
Zhang L; Zhang S
Nucleic Acids Res; 2019 Jul; 47(13):6606-6617. PubMed ID: 31175825
[TBL] [Abstract][Full Text] [Related]
2. SignalSpider: probabilistic pattern discovery on multiple normalized ChIP-Seq signal profiles.
Wong KC; Li Y; Peng C; Zhang Z
Bioinformatics; 2015 Jan; 31(1):17-24. PubMed ID: 25192742
[TBL] [Abstract][Full Text] [Related]
3. FGMD: A novel approach for functional gene module detection in cancer.
Jin D; Lee H
PLoS One; 2017; 12(12):e0188900. PubMed ID: 29244808
[TBL] [Abstract][Full Text] [Related]
4. Elucidating Genome-Wide Protein-RNA Interactions Using Differential Evolution.
Li X; Wong KC
IEEE/ACM Trans Comput Biol Bioinform; 2019; 16(1):272-282. PubMed ID: 29990254
[TBL] [Abstract][Full Text] [Related]
5. A novel statistical method for quantitative comparison of multiple ChIP-seq datasets.
Chen L; Wang C; Qin ZS; Wu H
Bioinformatics; 2015 Jun; 31(12):1889-96. PubMed ID: 25682068
[TBL] [Abstract][Full Text] [Related]
6. CARIP-Seq and ChIP-Seq: Methods to Identify Chromatin-Associated RNAs and Protein-DNA Interactions in Embryonic Stem Cells.
Kidder BL
J Vis Exp; 2018 May; (135):. PubMed ID: 29889205
[TBL] [Abstract][Full Text] [Related]
7. Approximate distance correlation for selecting highly interrelated genes across datasets.
Shen Q; Zhang S
PLoS Comput Biol; 2021 Nov; 17(11):e1009548. PubMed ID: 34752449
[TBL] [Abstract][Full Text] [Related]
8. A Quantitative Profiling Tool for Diverse Genomic Data Types Reveals Potential Associations between Chromatin and Pre-mRNA Processing.
Kremsky I; Bellora N; Eyras E
PLoS One; 2015; 10(7):e0132448. PubMed ID: 26207626
[TBL] [Abstract][Full Text] [Related]
9. Using combined evidence from replicates to evaluate ChIP-seq peaks.
Jalili V; Matteucci M; Masseroli M; Morelli MJ
Bioinformatics; 2015 Sep; 31(17):2761-9. PubMed ID: 25957351
[TBL] [Abstract][Full Text] [Related]
10. Use model-based Analysis of ChIP-Seq (MACS) to analyze short reads generated by sequencing protein-DNA interactions in embryonic stem cells.
Liu T
Methods Mol Biol; 2014; 1150():81-95. PubMed ID: 24743991
[TBL] [Abstract][Full Text] [Related]
11. Differential peak calling of ChIP-seq signals with replicates with THOR.
Allhoff M; Seré K; F Pires J; Zenke M; G Costa I
Nucleic Acids Res; 2016 Nov; 44(20):e153. PubMed ID: 27484474
[TBL] [Abstract][Full Text] [Related]
12. Analysis of ChIP-Seq and RNA-Seq Data with BioWardrobe.
Vallabh S; Kartashov AV; Barski A
Methods Mol Biol; 2018; 1783():343-360. PubMed ID: 29767371
[TBL] [Abstract][Full Text] [Related]
13. Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data.
Gong W; Koyano-Nakagawa N; Li T; Garry DJ
BMC Bioinformatics; 2015 Mar; 16():74. PubMed ID: 25887857
[TBL] [Abstract][Full Text] [Related]
14. CMT: a constrained multi-level thresholding approach for ChIP-Seq data analysis.
Rezaeian I; Rueda L
PLoS One; 2014; 9(4):e93873. PubMed ID: 24736605
[TBL] [Abstract][Full Text] [Related]
15. Computational analysis of protein-DNA interactions from ChIP-seq data.
Rougemont J; Naef F
Methods Mol Biol; 2012; 786():263-73. PubMed ID: 21938632
[TBL] [Abstract][Full Text] [Related]
16. TSEE: an elastic embedding method to visualize the dynamic gene expression patterns of time series single-cell RNA sequencing data.
An S; Ma L; Wan L
BMC Genomics; 2019 Apr; 20(Suppl 2):224. PubMed ID: 30967106
[TBL] [Abstract][Full Text] [Related]
17. Universal count correction for high-throughput sequencing.
Hashimoto TB; Edwards MD; Gifford DK
PLoS Comput Biol; 2014 Mar; 10(3):e1003494. PubMed ID: 24603409
[TBL] [Abstract][Full Text] [Related]
18. Chromatin analyses of Zymoseptoria tritici: Methods for chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq).
Soyer JL; Möller M; Schotanus K; Connolly LR; Galazka JM; Freitag M; Stukenbrock EH
Fungal Genet Biol; 2015 Jun; 79():63-70. PubMed ID: 25857259
[TBL] [Abstract][Full Text] [Related]
19. scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data.
Ye W; Ji G; Ye P; Long Y; Xiao X; Li S; Su Y; Wu X
BMC Genomics; 2019 May; 20(1):347. PubMed ID: 31068142
[TBL] [Abstract][Full Text] [Related]
20. De novo prediction of cis-regulatory elements and modules through integrative analysis of a large number of ChIP datasets.
Niu M; Tabari ES; Su Z
BMC Genomics; 2014 Dec; 15():1047. PubMed ID: 25442502
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]