Modeling the interactions between drugs, targets, and diseases is paramount
in drug discovery and has significant implications for precision medicine and
personalized treatments. Current approaches frequently consider drug-target or
drug-disease interactions individually, ignoring the interdependencies among
all three entities. Within human metabolic systems, drugs interact with protein
targets in cells, influencing target activities and subsequently impacting
biological pathways to promote healthy functions and treat diseases. Moving
beyond binary relationships and exploring tighter triple relationships is
essential to understanding drugs' mechanism of action (MoAs). Moreover,
identifying the heterogeneity of drugs, targets, and diseases, along with their
distinct characteristics, is critical to model these complex interactions
appropriately. To address these challenges, we effectively model the
interconnectedness of all entities in a heterogeneous graph and develop a novel
Heterogeneous Graph Triplet Attention Network (\texttt{HeTriNet}).
\texttt{HeTriNet} introduces a novel triplet attention mechanism within this
heterogeneous graph structure. Beyond pairwise attention as the importance of
an entity for the other one, we define triplet attention to model the
importance of pairs for entities in the drug-target-disease triplet prediction
problem. Experimental results on real-world datasets show that
\texttt{HeTriNet} outperforms several baselines, demonstrating its remarkable
proficiency in uncovering novel drug-target-disease relationships.

通过构建一个异构图，并引入新的三元关注机制，我们开发了一种名为 HeTriNet 的模型，可以更好地理解药物 - 靶标 - 疾病之间的相互作用，并在预测这种三元关系问题上显示出了出色的性能。

HeTriNet：药物 - 靶点 - 疾病相互作用的异构图三元注意力网络

HeTriNet: Heterogeneous Graph Triplet Attention Network for  Drug-Target-Disease Interaction

Contextual bandit algorithms are commonly used in digital health to recommend
personalized treatments. However, to ensure the effectiveness of the
treatments, patients are often requested to take actions that have no immediate
benefit to them, which we refer to as pro-treatment actions. In practice,
clinicians have a limited budget to encourage patients to take these actions
and collect additional information. We introduce a novel optimization and
learning algorithm to address this problem. This algorithm effectively combines
the strengths of two algorithmic approaches in a seamless manner, including 1)
an online primal-dual algorithm for deciding the optimal timing to reach out to
patients, and 2) a contextual bandit learning algorithm to deliver personalized
treatment to the patient. We prove that this algorithm admits a sub-linear
regret bound. We illustrate the usefulness of this algorithm on both synthetic
and real-world data.

文章介绍了一种结合在线 Primal-dual 算法和上下文强化学习算法的优化和学习算法来解决数字健康领域中的个性化治疗问题，并证明该算法具有亚线性后悔界限。