Predicting and understanding transcription factor interactions based on sequence level determinants of combinatorial control | Zendy

Aalt D. J. van Dijk | Zendy; Cajo J. F. ter Braak | Zendy; Richard G. H. Immink | Zendy; Gerco C. Angenent | Zendy; Roeland C. H. J. van Ham | Zendy

Open Access

Predicting and understanding transcription factor interactions based on sequence level determinants of combinatorial control

Author(s) -

Aalt D. J. van Dijk,

Cajo J. F. ter Braak,

Richard G. H. Immink,

Gerco C. Angenent,

Roeland C. H. J. van Ham

Publication year - 2007

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btm539

Subject(s) - transcription factor , computational biology , computer science , feature selection , sequence (biology) , motif (music) , biology , random forest , sequence motif , data mining , genetics , gene , machine learning , physics , acoustics

Transcription factor interactions are the cornerstone of combinatorial control, which is a crucial aspect of the gene regulatory system. Understanding and predicting transcription factor interactions based on their sequence alone is difficult since they are often part of families of factors sharing high sequence identity. Given the scarcity of experimental data on interactions compared to available sequence data, however, it would be most useful to have accurate methods for the prediction of such interactions.

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