KDDN: An open-source Cytoscape app for constructing differential dependency networks with significant rewiring

Ye Tian, Bai Zhang, Eric P. Hoffman, Robert Clarke, Zhen Zhang, Ie Ming Shih, Jianhua Xuan, David M. Herrington, Yue Wang

Research output: Contribution to journalArticle

Abstract

We have developed an integrated molecular network learning method, within a well-grounded mathematical framework, to construct differential dependency networks with significant rewiring. This knowledge-fused differential dependency networks (KDDN) method, implemented as a Java Cytoscape app, can be used to optimally integrate prior biological knowledge with measured data to simultaneously construct both common and differential networks, to quantitatively assign model parameters and significant rewiring p-values and to provide user-friendly graphical results. The KDDN algorithm is computationally efficient and provides users with parallel computing capability using ubiquitous multi-core machines. We demonstrate the performance of KDDN on various simulations and real gene expression datasets, and further compare the results with those obtained by the most relevant peer methods. The acquired biologically plausible results provide new insights into network rewiring as a mechanistic principle and illustrate KDDN's ability to detect them efficiently and correctly. Although the principal application here involves microarray gene expressions, our methodology can be readily applied to other types of quantitative molecular profiling data.

Original languageEnglish (US)
Pages (from-to)287-289
Number of pages3
JournalBioinformatics
Volume31
Issue number2
DOIs
StatePublished - Jan 15 2015

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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