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Abstract Heterogeneous graphs are widely employed in applications such as social networks, recommendation systems, and bioinformatics. However, node attributes in real-world heterogeneous graphs are often missing or corr...

Abstract Collaborative filtering and neural recommendation models have advanced substantially over the past decade, yet they frequently struggle to capture the fine-grained structure of user preferences and the rich, mul...

Abstract Graph Neural Networks have emerged as a powerful paradigm for artificial intelligence driven drug discovery, offering molecular representation learning that surpasses many conventional approaches. Traditional ex...

Abstract Background Long COVID is an infection-associated chronic condition with uncertain evolution, leading to ambiguity in case definitions and various hypotheses about its pathophysiology. Despite this diversity, cau...

Abstract Finding interesting phenomena is the core of scientific discovery, but the notion of interestingness is vaguely defined and heavily reliant on manual judgment. We present InterFeat, an integrative pipeline for a...

Abstract The pre-training molecular representation learning (MRL) has shown considerable potential in computer-aided drug discovery. Recently, many multimodal pre-training MRL methods have been presented, incorporating m...

Abstract The proposed study is an integrated graph-theoretical and statistical model of predictive modeling and ranking of influenza strain drugs based on temperature-based topological indices. Chemical graphs were used ...