151 related articles for article (PubMed ID: 34864886)
1. A deep learning model to identify gene expression level using cobinding transcription factor signals.
Zhang L; Yang Y; Chai L; Li Q; Liu J; Lin H; Liu L
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34864886
[TBL] [Abstract][Full Text] [Related]
2. Inferring the regulatory interaction models of transcription factors in transcriptional regulatory networks.
Awad S; Panchy N; Ng SK; Chen J
J Bioinform Comput Biol; 2012 Oct; 10(5):1250012. PubMed ID: 22849367
[TBL] [Abstract][Full Text] [Related]
3. Revealing transcription factor and histone modification co-localization and dynamics across cell lines by integrating ChIP-seq and RNA-seq data.
Zhang L; Xue G; Liu J; Li Q; Wang Y
BMC Genomics; 2018 Dec; 19(Suppl 10):914. PubMed ID: 30598100
[TBL] [Abstract][Full Text] [Related]
4. Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data.
Zhang Y; Xuan J; de los Reyes BG; Clarke R; Ressom HW
BMC Bioinformatics; 2008 Apr; 9():203. PubMed ID: 18426580
[TBL] [Abstract][Full Text] [Related]
5. TF-centered downstream gene set enrichment analysis: Inference of causal regulators by integrating TF-DNA interactions and protein post-translational modifications information.
Liu Q; Tan Y; Huang T; Ding G; Tu Z; Liu L; Li Y; Dai H; Xie L
BMC Bioinformatics; 2010 Dec; 11 Suppl 11(Suppl 11):S5. PubMed ID: 21172055
[TBL] [Abstract][Full Text] [Related]
6. Uncovering co-regulatory modules and gene regulatory networks in the heart through machine learning-based analysis of large-scale epigenomic data.
Vahab N; Bonu T; Kuhlmann L; Ramialison M; Tyagi S
Comput Biol Med; 2024 Mar; 171():108068. PubMed ID: 38354497
[TBL] [Abstract][Full Text] [Related]
7. Improved recovery of cell-cycle gene expression in Saccharomyces cerevisiae from regulatory interactions in multiple omics data.
Panchy NL; Lloyd JP; Shiu SH
BMC Genomics; 2020 Feb; 21(1):159. PubMed ID: 32054475
[TBL] [Abstract][Full Text] [Related]
8. DiNeR: a Differential graphical model for analysis of co-regulation Network Rewiring.
Zhang J; Liu J; Lee D; Lou S; Chen Z; Gürsoy G; Gerstein M
BMC Bioinformatics; 2020 Jul; 21(1):281. PubMed ID: 32615918
[TBL] [Abstract][Full Text] [Related]
9. Enhancing the interpretability of transcription factor binding site prediction using attention mechanism.
Park S; Koh Y; Jeon H; Kim H; Yeo Y; Kang J
Sci Rep; 2020 Aug; 10(1):13413. PubMed ID: 32770026
[TBL] [Abstract][Full Text] [Related]
10. Probing transcription factor combinatorics in different promoter classes and in enhancers.
Vandel J; Cassan O; Lèbre S; Lecellier CH; Bréhélin L
BMC Genomics; 2019 Feb; 20(1):103. PubMed ID: 30709337
[TBL] [Abstract][Full Text] [Related]
11. Cross-Cell-Type Prediction of TF-Binding Site by Integrating Convolutional Neural Network and Adversarial Network.
Lan G; Zhou J; Xu R; Lu Q; Wang H
Int J Mol Sci; 2019 Jul; 20(14):. PubMed ID: 31336830
[TBL] [Abstract][Full Text] [Related]
12. Unraveling transcriptional regulatory programs by integrative analysis of microarray and transcription factor binding data.
Li H; Zhan M
Bioinformatics; 2008 Sep; 24(17):1874-80. PubMed ID: 18586698
[TBL] [Abstract][Full Text] [Related]
13. DeepTFactor: A deep learning-based tool for the prediction of transcription factors.
Kim GB; Gao Y; Palsson BO; Lee SY
Proc Natl Acad Sci U S A; 2021 Jan; 118(2):. PubMed ID: 33372147
[TBL] [Abstract][Full Text] [Related]
14. Evaluation of deep learning approaches for modeling transcription factor sequence specificity.
Zhang Y; Mo Q; Xue L; Luo J
Genomics; 2021 Nov; 113(6):3774-3781. PubMed ID: 34534646
[TBL] [Abstract][Full Text] [Related]
15. Network component analysis provides quantitative insights on an Arabidopsis transcription factor-gene regulatory network.
Misra A; Sriram G
BMC Syst Biol; 2013 Nov; 7():126. PubMed ID: 24228871
[TBL] [Abstract][Full Text] [Related]
16. Inferring microRNA and transcription factor regulatory networks in heterogeneous data.
Le TD; Liu L; Liu B; Tsykin A; Goodall GJ; Satou K; Li J
BMC Bioinformatics; 2013 Mar; 14():92. PubMed ID: 23497388
[TBL] [Abstract][Full Text] [Related]
17. TF-finder: a software package for identifying transcription factors involved in biological processes using microarray data and existing knowledge base.
Cui X; Wang T; Chen HS; Busov V; Wei H
BMC Bioinformatics; 2010 Aug; 11():425. PubMed ID: 20704747
[TBL] [Abstract][Full Text] [Related]
18. Transcription factor expression landscape in Drosophila embryonic cell lines.
Drewell RA; Klonaros D; Dresch JM
BMC Genomics; 2024 Mar; 25(1):307. PubMed ID: 38521929
[TBL] [Abstract][Full Text] [Related]
19. Latent Semantic Indexing of PubMed abstracts for identification of transcription factor candidates from microarray derived gene sets.
Roy S; Heinrich K; Phan V; Berry MW; Homayouni R
BMC Bioinformatics; 2011 Oct; 12 Suppl 10(Suppl 10):S19. PubMed ID: 22165960
[TBL] [Abstract][Full Text] [Related]
20. High-resolution transcription factor binding sites prediction improved performance and interpretability by deep learning method.
Zhang Y; Wang Z; Zeng Y; Zhou J; Zou Q
Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34272562
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
