Generalizing and learning protein-DNA binding sequence representations by an evolutionary algorithm

Ka Chun Wong, Chengbin Peng, Manhon Wong, Kwongsak Leung

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


Protein-DNA bindings are essential activities. Understanding them forms the basis for further deciphering of biological and genetic systems. In particular, the protein-DNA bindings between transcription factors (TFs) and transcription factor binding sites (TFBSs) play a central role in gene transcription. Comprehensive TF-TFBS binding sequence pairs have been found in a recent study. However, they are in one-to-one mappings which cannot fully reflect the many-to-many mappings within the bindings. An evolutionary algorithm is proposed to learn generalized representations (many-to-many mappings) from the TF-TFBS binding sequence pairs (one-to-one mappings). The generalized pairs are shown to be more meaningful than the original TF-TFBS binding sequence pairs. Some representative examples have been analyzed in this study. In particular, it shows that the TF-TFBS binding sequence pairs are not presumably in one-to-one mappings. They can also exhibit many-to-many mappings. The proposed method can help us extract such many-to-many information from the one-to-one TF-TFBS binding sequence pairs found in the previous study, providing further knowledge in understanding the bindings between TFs and TFBSs. © 2011 Springer-Verlag.
Original languageEnglish (US)
Pages (from-to)1631-1642
Number of pages12
JournalSoft Computing
Issue number8
StatePublished - Feb 5 2011

ASJC Scopus subject areas

  • Geometry and Topology
  • Theoretical Computer Science
  • Software


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