10.6084/m9.figshare.1423388.v4
Na Zou
Na
Zou
Yun Zhu
Yun
Zhu
Ji Zhu
Ji
Zhu
Mustafa Baydogan
Mustafa
Baydogan
Wei Wang
Wei
Wang
Jing Li
Jing
Li
A Transfer Learning Approach for Predictive Modeling of Degenerate Biological Systems
Taylor & Francis Group
2015
systems modeling
Cell lines
Theoretical properties
predictive modeling
Transfer Learning Approach
transcription factors
Gene expression
article studies transfer
prediction accuracy
Bayesian framework
Supplementary materials
model
2015-10-08 14:46:46
Dataset
https://tandf.figshare.com/articles/dataset/A_Transfer_Learning_Approach_for_Predictive_Modeling_of_Degenerate_Biological_Systems/1423388
<div><p>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.</p></div>