no code implementations • 21 Feb 2025 • Yingying Sun, Jun A, Zhiwei Liu, Rui Sun, Liujia Qian, Samuel H. Payne, Wout Bittremieux, Markus Ralser, Chen Li, Yi Chen, Zhen Dong, Yasset Perez-Riverol, Asif Khan, Chris Sander, Ruedi Aebersold, Juan Antonio Vizcaíno, Jonathan R Krieger, Jianhua Yao, Han Wen, Linfeng Zhang, Yunping Zhu, Yue Xuan, Benjamin Boyang Sun, Liang Qiao, Henning Hermjakob, Haixu Tang, Huanhuan Gao, Yamin Deng, Qing Zhong, Cheng Chang, Nuno Bandeira, Ming Li, Weinan E, Siqi Sun, Yuedong Yang, Gilbert S. Omenn, Yue Zhang, Ping Xu, Yan Fu, Xiaowen Liu, Christopher M. Overall, Yu Wang, Eric W. Deutsch, Luonan Chen, Jürgen Cox, Vadim Demichev, Fuchu He, Jiaxing Huang, Huilin Jin, Chao Liu, Nan Li, Zhongzhi Luan, Jiangning Song, Kaicheng Yu, Wanggen Wan, Tai Wang, Kang Zhang, Le Zhang, Peter A. Bell, Matthias Mann, Bing Zhang, Tiannan Guo
Artificial intelligence (AI) is transforming scientific research, including proteomics.
no code implementations • 7 Feb 2024 • Jiahua Rao, Jiancong Xie, Hanjing Lin, Shuangjia Zheng, Zhen Wang, Yuedong Yang
While such methods could improve GNN predictions, they usually don't perform well on explanations.
1 code implementation • CVPR 2024 • Shenshen Bu, Taiji Li, Yuedong Yang, Zhiming Dai
In this paper we propose an Instance-level Expert Knowledge and Aggregate Discriminative Attention framework (EKAGen) for radiology report generation.
1 code implementation • 16 Oct 2023 • Zhiguang Fan, Yuedong Yang, Mingyuan Xu, Hongming Chen
Particularly in biomedical relationship prediction tasks, NC-KGE outperforms all baselines on datasets such as PharmKG8k-28, DRKG17k-21, and BioKG72k-14, especially in predicting drug combination relationships.
1 code implementation • 1 Aug 2023 • Zhiguang Fan, Yuedong Yang, Mingyuan Xu, Hongming Chen
In this paper, an equivariant consistency model (EC-Conf) was proposed as a fast diffusion method for low-energy conformation generation.
no code implementations • 13 May 2023 • Guihong Li, Kartikeya Bhardwaj, Yuedong Yang, Radu Marculescu
Anytime neural networks (AnytimeNNs) are a promising solution to adaptively adjust the model complexity at runtime under various hardware resource constraints.
no code implementations • 27 Apr 2023 • Jiahua Rao, Zifei Shan, Longpo Liu, Yao Zhou, Yuedong Yang
With the recent progress in large-scale vision and language representation learning, Vision Language Pre-training (VLP) models have achieved promising improvements on various multi-modal downstream tasks.
1 code implementation • 26 Jan 2023 • Guihong Li, Yuedong Yang, Kartikeya Bhardwaj, Radu Marculescu
Based on this theoretical analysis, we propose a new zero-shot proxy, ZiCo, the first proxy that works consistently better than #Params.
1 code implementation • CVPR 2023 • Yuedong Yang, Guihong Li, Radu Marculescu
Despite its importance for federated learning, continuous learning and many other applications, on-device training remains an open problem for EdgeAI.
1 code implementation • 12 May 2022 • Jiahua Rao, Shuangjia Zheng, Sijie Mai, Yuedong Yang
To address these problems, we propose a novel Communicative Subgraph representation learning for Multi-relational Inductive drug-Gene interactions prediction (CoSMIG), where the predictions of drug-gene relations are made through subgraph patterns, and thus are naturally inductive for unseen drugs/genes without retraining or utilizing external domain features.
no code implementations • 31 Jan 2022 • Zihui Xue, Yuedong Yang, Mengtian Yang, Radu Marculescu
Graph Neural Networks (GNNs) have demonstrated a great potential in a variety of graph-based applications, such as recommender systems, drug discovery, and object recognition.
no code implementations • 30 Nov 2021 • Shuangjia Zheng, Ying Song, Zhang Pan, Chengtao Li, Le Song, Yuedong Yang
Optimizing chemical molecules for desired properties lies at the core of drug development.
no code implementations • 26 Jul 2021 • Shuangjia Zheng, Sijie Mai, Ya Sun, Haifeng Hu, Yuedong Yang
In this way, we find the model can quickly adapt to few-shot relationships using only a handful of known facts with inductive settings.
1 code implementation • 19 Jul 2021 • Jianwen Chen, Shuangjia Zheng, Ying Song, Jiahua Rao, Yuedong Yang
For this sake, we propose a Communicative Message Passing Transformer (CoMPT) neural network to improve the molecular graph representation by reinforcing message interactions between nodes and edges based on the Transformer architecture.
2 code implementations • 1 Jul 2021 • Jiahua Rao, Shuangjia Zheng, Yuedong Yang
Advances in machine learning have led to graph neural network-based methods for drug discovery, yielding promising results in molecular design, chemical synthesis planning, and molecular property prediction.
no code implementations • 26 May 2021 • Shuangjia Zheng, Tao Zeng, Chengtao Li, Binghong Chen, Connor W. Coley, Yuedong Yang, Ruibo Wu
Nature, a synthetic master, creates more than 300, 000 natural products (NPs) which are the major constituents of FDA-proved drugs owing to the vast chemical space of NPs.
1 code implementation • 16 Dec 2020 • Sijie Mai, Shuangjia Zheng, Yuedong Yang, Haifeng Hu
Relation prediction for knowledge graphs aims at predicting missing relationships between entities.
1 code implementation • 2 Jul 2019 • Shuangjia Zheng, Jiahua Rao, Zhongyue Zhang, Jun Xu, Yuedong Yang
Synthesis planning is the process of recursively decomposing target molecules into available precursors.