Titlebook: Database Systems for Advanced Applications; 29th International C Makoto Onizuka,Jae-Gil Lee,Kejing Lu Conference proceedings 2024 The Edito

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ents. However, existing works in this domain has encountered certain limitations when applied in practical settings. Firstly, most studies have primarily focused on binary treatment scenarios, but real-world industrial applications often involve multi-valued treatments, rendering these approaches in

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Spartan

ly focused on modeling CPIs either?from intramolecular or intermolecular interactions, disregarding?the diversity of interactions and the fine dependencies between?these two types of interactions, thereby limiting the accuracy of?CPI predictions. We argue that properly considering both intramolecula

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Muhammad Arshad,William T. Frankenberger Jr.cs advancements. Despite its potential, traditional clustering methods in scRNA-seq data analysis often neglect the structural information embedded in gene expression profiles, crucial for understanding cellular correlations and dependencies. Existing strategies, including graph neural networks, fac

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H. Kende,J.-P. Metraux,I. Raskin images, and subsequently using CNN models to extract features for prediction. Nevertheless, these CNN-based methods are fraught with several critical issues: 1) ignore the complexity of protein structures; 2) susceptible to rotation; 3) deficient in handling global and long-range geometric informat

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Growth, Metabolism, and Structure,chieved promising performance in DDI prediction. However, limited attention has been given to the integration of substructure information and drug relationships to capture complex DDI patterns using self-supervised learning techniques. To this end, we propose a novel hierarchical cross-level graph c

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Alexandria, das Museion, Euklid,stic by capturing inconsistencies between text and image information. Despite achieving significant success, most existing methods primarily focus on modeling cross-modal relationships through various interaction mechanisms, ignoring the inter-modal gap caused by the representation and granularity o