Local causal structure learning aims to identify direct causes (parents) and effects (children) of a target variable. Recent advances in this field rely on learning the MB (Markov Blanket) of unidentified variables. However, most existing methods require extensive search to discover spouses during MB learning, and they often need to learn the MB within the PC (parents and children) set of a target variable to identify parents, which becomes computationally expensive when the parent set is large and complex. To address this issue, we propose a novel local causal structure learning algorithm with Efficient Parents Discovery, named EPD. Specifically, EPD introduces an MB discovery subroutine, MBDis, which first identifies some parents of the target variable using the V-structure, and then performs feature selection to exclude candidate spouses that are weakly related to the target variable, thereby reducing the size of the candidate spouse set and accelerating spouse discovery. Additionally, EPD incorporates an IdePC subroutine, which learns the PC sets of the unresolved variables to identify additional parents, reducing the search space of parents. With the proposed MBDis and IdePC subroutines, EPD adopts an MB-by-PC learning strategy; it starts from discovering the MB of the target variable and then learns the PC sets of undetermined variables. This process continues iteratively until the parents and children of the target variable are identified. Using 7 Bayesian networks and 1 real-world data, the experiments have verified the effectiveness of EPD, in comparison with 10 state-of-the-art methods.
Shuai Yang, Xin-Yu Miao, Xianjie Guo et al.· ACM Transactions on Intellig...· 0 citations
Drug-drug interactions (DDIs) are a major cause of adverse drug events in clinical practice, especially under polypharmacy settings where patients receive multiple medications simultaneously. Reliable computational prediction of DDIs is therefore essential for improving medication safety and supporting clinical decision-making. Despite recent advances in computational DDI prediction, existing methods often struggle to jointly model multi-granularity pharmacological semantics and stereochemical molecular characteristics, limiting their ability to generalize to previously unseen drugs under cold-start scenarios. To address these limitations, we propose DSMV-DDI, a multimodal representation learning framework for drug-drug interaction prediction that integrates biomedical knowledge graph topology, chemical substructure features, dual-level pharmacological semantic representations, and stereochemical molecular visual representations derived from three-dimensional molecular conformations. In particular, the proposed dual-level semantic strategy jointly characterizes interaction-level pharmacological associations and intrinsic single-drug functional properties, enabling complementary modeling of pharmacological information across different semantic granularities. Furthermore, molecular visual representation learning captures geometric and spatial characteristics beyond topology-based molecular representations, improving generalization to topologically unseen drugs. Extensive experiments on real-world DDI datasets demonstrate that DSMV-DDI outperforms state-of-the-art methods, achieving an accuracy of 0.967 and an AUPR of 0.992 under the conventional setting. The proposed framework also maintains strong performance under both partial and complete cold-start settings. Ablation analyses show that dual-level pharmacological semantics contribute most to overall performance, while molecular visual representations provide complementary geometric information that further improves prediction accuracy.
Fangni Chen, Tingting Jiang, Shuai Yang et al.· Journal of Molecular Graphic...· 0 citations
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