Combining Multiple Types of Biological Data in Constraint-Based Learning of Gene Regulatory Networks

Tan M., AlShalalfa M., Alhajj R., POLAT F.

IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, Idaho, Amerika Birleşik Devletleri, 15 - 17 Eylül 2008, ss.131-132 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/cibcb.2008.4675764
  • Basıldığı Şehir: Idaho
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.131-132
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Due to the complex structure and scale of gene regulatory networks, we support the argument that combination of multiple types of biological data to derive satisfactory network structures is necessary to understand the regulatory mechanisms of cellular systems. In this paper, we propose a simple but effective method of combining two types of biological data, namely microarray and transcription factor (TF) binding data, to construct gene regulatory networks. The proposed algorithm is based on and extends the well-known PC algorithm [23]. Further, we developed a method for measuring the significance of the interactions between the genes and the TFs. The reported test results on both synthetic and real data sets demonstrate the applicability and effectiveness of the proposed approach; we also report the results of some comparative analysis that highlights the power of the proposed approach.