TRANSFAC: an integrated system for gene expression regulation. TRANSFAC is a database on transcription factors, their genomic binding sites and DNA-binding profiles ( ). Its content has been enhanced, in particular by information about training sequences used for the construction of nucleotide matrices as well as by data on plant sites and factors. Moreover, TRANSFAC has been extended by two new modules: PathoDB provides data on pathologically relevant mutations in regulatory regions and transcription factor genes, whereas S/MARt DB compiles features of scaffold/matrix attached regions (S/MARs) and the proteins binding to them. Additionally, the databases TRANSPATH, about signal transduction, and CYTOMER, about organs and cell types, have been extended and are increasingly integrated with the TRANSFAC data sources.

References in zbMATH (referenced in 74 articles )

Showing results 1 to 20 of 74.
Sorted by year (citations)

1 2 3 4 next

  1. Murugan, Rajamanickam: Theory on the looping mediated directional-dependent propulsion of transcription factors along DNA (2020)
  2. Yuan, Ye; Bar-Joseph, Ziv: Deep learning for inferring gene relationships from single-cell expression data (2019)
  3. Keith, Jonathan M. (ed.): Bioinformatics. Volume I. Data, sequence analysis, and evolution (2017)
  4. Toivonen, Jarkko; Taipale, Jussi; Ukkonen, Esko: Seed-driven learning of position probability matrices from large sequence sets (2017)
  5. Pesch, Robert: Cross-species network and transcript transfer (2016)
  6. Young, William Chad; Raftery, Adrian E.; Yeung, Ka Yee: A posterior probability approach for gene regulatory network inference in genetic perturbation data (2016)
  7. Ferkingstad, Egil; Holden, Lars; Sandve, Geir Kjetil: Monte Carlo null models for genomic data (2015)
  8. Carvalho, Alexandra M.; Adão, Pedro; Mateus, Paulo: Hybrid learning of Bayesian multinets for binary classification (2014)
  9. González-Álvarez, David L.; Vega-Rodríguez, Miguel A.; Rubio-Largo, Álvaro: Convergence analysis of some multiobjective evolutionary algorithms when discovering motifs (2014) ioport
  10. Hsu, Yi-Yu; Chen, Wei-Jhih; Chen, Shu-Hui; Kao, Hung-Yu: Using hidden Markov models to predict DNA-binding proteins with sequence and structure information (2014) ioport
  11. Kasabov, Nikola (ed.): Springer handbook of bio-/neuro-informatics (2014)
  12. Wang, Y. X. Rachel; Huang, Haiyan: Review on statistical methods for gene network reconstruction using expression data (2014)
  13. Abbass, Mostafa M.; Bahig, Hazem M.: An efficient algorithm to identify DNA motifs (2013)
  14. Michailidis, George; d’Alché-Buc, Florence: Autoregressive models for gene regulatory network inference: sparsity, stability and causality issues (2013)
  15. Mahdevar, Ghasem; Sadeghi, Mehdi; Nowzari-Dalini, Abbas: Transcription factor binding sites detection by using alignment-based approach (2012)
  16. Wang, Dianhui; Do, Hai Thanh: Computational localization of transcription factor binding sites using extreme learning machines (2012) ioport
  17. Giraud, Mathieu; Varré, Jean-Stéphane: Parallel position weight matrices algorithms (2011) ioport
  18. Li, Wentian: On parameters of the human genome (2011)
  19. Porzelius, Christine; Johannes, Marc; Binder, Harald; Beißbarth, Tim: Supporting information leveraging external knowledge on molecular interactions in classification methods for risk prediction of patients (2011)
  20. Wong, Ka-Chun; Peng, Chengbin; Wong, Man-Hon; Leung, Kwong-Sak: Generalizing and learning protein-DNA binding sequence representations by an evolutionary algorithm (2011) ioport

1 2 3 4 next