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Jianping Yu

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2 papers

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JBHI Journal 2025 Journal Article

Synergistic Drug Combination Prediction via Dual-Level Feature Aggregation and Knowledge Graph-Based Deep Neural Network

Ying Zuo
Yan Zhang
Li Wang
Jianping Yu
Jiawei Luo
Qiu Xiao

Identifying synergistic drug combinations is a critical but difficult challenge in cancer treatment, owing to the sheer complexity and enormous number of possible drug combinations. However, most existing computational methods rely on a single data perspective and often overlooking the complexity of interactions between different biological entities. Furthermore, they fail to fully integrate the intrinsic properties of drugs and cell lines with the broader biological relationships that play a crucial role in drug synergy. To address these challenges, we propose a novel framework called LGSyn that integrates two types of information: local features, including molecular fingerprints, descriptors, and gene expression profiles, as well as global features that encompass broader biological interactions, including drug-protein, protein-cell line, protein-protein, and cell line-tissue interactions. By combining these two types of features, LGSyn leverages the full spectrum of biological knowledge to predict drug synergy. In LGSyn, we developed three fusion strategies to effectively integrate local and global information and identify the most suitable strategy. The resulting fused feature vectors are then fed into a deep neural network for training and synergy prediction. Experimental results demonstrate that the proposed method outperforms current state-of-the-art models, achieving superior accuracy and stability in drug synergy prediction.

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ICML Conference 2021 Conference Paper

Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation

Chao Chen 0016
Haoyu Geng
Nianzu Yang
Junchi Yan
Daiyue Xue
Jianping Yu
Xiaokang Yang 0001

User interests are usually dynamic in the real world, which poses both theoretical and practical challenges for learning accurate preferences from rich behavior data. Among existing user behavior modeling solutions, attention networks are widely adopted for its effectiveness and relative simplicity. Despite being extensively studied, existing attentions still suffer from two limitations: i) conventional attentions mainly take into account the spatial correlation between user behaviors, regardless the distance between those behaviors in the continuous time space; and ii) these attentions mostly provide a dense and undistinguished distribution over all past behaviors then attentively encode them into the output latent representations. This is however not suitable in practical scenarios where a user’s future actions are relevant to a small subset of her/his historical behaviors. In this paper, we propose a novel attention network, named \textit{self-modulating attention}, that models the complex and non-linearly evolving dynamic user preferences. We empirically demonstrate the effectiveness of our method on top-N sequential recommendation tasks, and the results on three large-scale real-world datasets show that our model can achieve state-of-the-art performance.

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