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20232026
most citedAccurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning

3 citations · 7 across the 12 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

DGNet: Discrete Green Networks for Data-Efficient Learning of Spatiotemporal PDEs

Yingjie Tan, Quanming Yao, Yaqing Wang

Spatiotemporal partial differential equations (PDEs) underpin a wide range of scientific and engineering applications. Neural PDE solvers offer a promising alternative to classical…

cs.LG2026

Self-Generative Adversarial Fine-Tuning for Large Language Models

Shiguang Wu, Yaqing Wang, Quanming Yao

Fine-tuning large language models (LLMs) for alignment typically relies on supervised fine-tuning or reinforcement learning from human feedback, both limited by the cost and scarci…

cs.LG2025

Attending on Multilevel Structure of Proteins enables Accurate Prediction of Cold-Start Drug-Target Interactions

Ziying Zhang, Yaqing Wang, Yuxuan Sun +2

Cold-start drug-target interaction (DTI) prediction focuses on interaction between novel drugs and proteins. Previous methods typically learn transferable interaction patterns betw…

cs.LG2024

Beyond Scaleup: Knowledge-aware Parsimony Learning from Deep Networks

Quanming Yao, Yongqi Zhang, Yaqing Wang +3

The brute-force scaleup of training datasets, learnable parameters and computation power, has become a prevalent strategy for developing more robust learning models. However, due t…

cs.LG2023★ 3 cited

Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning

Yaqing Wang, Zaifei Yang, Quanming Yao

Background: Discovering potential drug-drug interactions (DDIs) is a long-standing challenge in clinical treatments and drug developments. Recently, deep learning techniques have b…

cs.LG2023★ 2 cited

PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction

Shiguang Wu, Yaqing Wang, Quanming Yao

Molecular property prediction (MPP) plays a crucial role in biomedical applications, but it often encounters challenges due to a scarcity of labeled data. Existing works commonly a…