A Transfer Learning Approach for Predictive Modeling of Degenerate Biological Systems

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.

对新领域的建模往往面临数据稀缺和高维度的挑战。迁移学习旨在将新领域的数据与某些相关旧领域的知识相结合，以更好地对新领域进行建模。本文探讨了针对退化生物系统的迁移学习。退化是指系统中结构上不同的元素执行相同或相似的功能或产生相同或相似的结果的现象。退化存在于各种生物系统中，并促进了系统的异质性、复杂性和鲁棒性。退化生物系统的建模具有挑战性，且在支持此类系统迁移学习的模型方面研究甚少。在本文中，我们提出了一种在贝叶斯框架下整合迁移学习和退化特性的预测模型。对所提出模型的理论性质进行了研究。最后，我们展示了利用该模型对多个细胞系中转录因子与基因表达之间的预测关系进行建模的应用。该模型实现了良好的预测精度，并识别了已知以及可能的新退化机制。本文的补充材料可在网上获取。