Effective induction of gene regulatory networks using a novel recommendation method

Ozsoy M. G., POLAT F., Alhajj R.

INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, cilt.22, sa.2, ss.91-112, 2019 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 22 Sayı: 2
  • Basım Tarihi: 2019
  • Dergi Adı: INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.91-112
  • Anahtar Kelimeler: GRNs, gene regulatory networks, recommendation systems, collaborative filtering, multiple data sources, Pareto dominance, BAYESIAN NETWORKS, MODELS, INFERENCE
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

In this paper, we introduce a method based on recommendation systems to predict the structure of Gene Regulatory Networks (GRNs) making use of data from multiple sources. Our method is based on collaborative filtering approach enhanced with multiple criteria to predict the relationships of genes, i.e., which genes regulate others. We conduct experiments on two data sets to demonstrate the applicability and sustainability of our proposal. The first data set is composed of microarray data and Transcription Factor (TF) binding data, and it is evaluated by precision, recall and the F1-measure. The second data set is the Dream4 In Silico Network Challenge data set, and it is evaluated by the measures that are used during the challenge, namely the Area Under Precision and Recall curve (AUC-PR), the Area Under the Receiver Operating Characteristic curve (AUC-ROC) and their averages. The experimental results show that applying algorithms from the recommendation systems domain on the problem of inference of GRN structures is effective. Also, we observed that combining information from multiple data sets gives better results.