Abstract In biomedical research, increasing attention has been paid to the discovery of regulatory relationships among heterogeneous biological features. We present a new statistical framework to jointly learn multiple heterogeneous exponential Markov Random Fields. We establish an approximate likelihood inference problem regularized by an embedded group lasso penalty, and propose an efficient algorithm in the Alternating Direction Method of Multipliers framework. We also establish structure recovery consistency for the proposed joint network learning. The practical merits of the proposed integrative structural learning method are demonstrated through simulations and real applications to discovering regulatory relationships among heterogeneous biological variables from distinct but related types of cancer.

中文翻译：

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通过近似似然推理联合估计异构指数马尔可夫随机场

摘要 在生物医学研究中，发现异质生物特征之间的调控关系越来越受到关注。我们提出了一个新的统计框架来共同学习多个异构指数马尔可夫随机场。我们建立了一个由嵌入式组套索惩罚正则化的近似似然推理问题，并在乘法器的交替方向方法框架中提出了一种有效的算法。我们还为提议的联合网络学习建立了结构恢复一致性。通过模拟和实际应用来发现来自不同但相关类型的癌症的异质生物变量之间的调节关系，证明了所提出的综合结构学习方法的实际优点。