A Transfer Learning Approach for Predictive Modeling of Degenerate Biological Systems

Na Zou, Yun Zhu, Ji Zhu, Mustafa Baydogan, Wei Wang, Jing Li

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Modeling of a new domain can be challenging due to scarce data and high-dimensionality. Transfer learning aims to integrate data of the new domain with knowledge about some related old domains, to model the new domain better. This article studies transfer learning for degenerate biological systems. Degeneracy refers to the phenomenon that structurally different elements of the system perform the same/similar function or yield the same/similar output. Degeneracy exists in various biological systems and contributes to the heterogeneity, complexity, and robustness of the systems. Modeling of degenerate biological systems is challenging and models enabling transfer learning in such systems have been little studied. In this article, we propose a predictive model that integrates transfer learning and degeneracy under a Bayesian framework. Theoretical properties of the proposed model are studied. Finally, we present an application of modeling the predictive relationship between transcription factors and gene expression across multiple cell lines. The model achieves good prediction accuracy, and identifies known and possibly new degenerate mechanisms of the system. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)362-373
Number of pages12
Issue number3
StatePublished - Jul 3 2015


  • Bayesian
  • Regression

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics


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