Results 131 to 140 of about 9,194 (254)

FEWT: Frequency‐Enhanced Wavelet‐Based Transformer for Multimodal Wheeled Bimanual Manipulation

SmartBot, EarlyView.
An embodied mobile bimanual robot learns dexterous manipulation skills through imitation learning with multimodal perception. The integration of vision, proprioception, and tactile sensing establishes a comprehensive perception–action loop, enabling the robot to continuously understand, interact with, and adapt to complex physical environments.
Jiaxin Huang, Hanyu Liu, Yunsheng Ma, Jian Shen, Yilin Zheng, Jiayi Wen, Zhigong Song   +6 more
wiley   +1 more source

VGAEDTI: drug-target interaction prediction based on variational inference and graph autoencoder. [PDF]

BMC Bioinformatics, 2023
Zhang Y, Feng Y, Wu M, Deng Z, Wang S.
europepmc   +1 more source

Artificial intelligence empowers targeted protein degradation: Core technological innovations, multi‐scenario applications, and translational prospects

Smart Molecules, EarlyView.
AI is transforming TPD by improving the design, prediction, and optimization of degraders such as PROTACs, molecular glues, and LYTACs. This review summarizes key AI‐driven advances, highlights applications across drug discovery stages, and discusses remaining challenges and future directions for accelerating the development of therapies against ...
Shuanglin Qin, Rui Peng, Guangshuai Zhang, Si Yan, Jin Xiao, Zishu Liu, Ziyu Tan, Qingqing Xie, Shangjie Li, Wei Luo, Xiaohe Xiao   +10 more
wiley   +1 more source

MULGA, a unified multi-view graph autoencoder-based approach for identifying drug-protein interaction and drug repositioning. [PDF]

Bioinformatics, 2023
Ma J, Li C, Zhang Y, Wang Z, Li S, Guo Y, Zhang L, Liu H, Gao X, Song J.   +9 more
europepmc   +1 more source

Image and video analysis using graph neural network for Internet of Medical Things and computer vision applications

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma, Ashutosh Sharma, Alexey Tselykh, Alexander Bozhenyuk, Byung‐Gyu Kim   +4 more
wiley   +1 more source

Boosted unsupervised feature selection for tumor gene expression profiles

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract In an unsupervised scenario, it is challenging but essential to eliminate noise and redundant features for tumour gene expression profiles. However, the current unsupervised feature selection methods treat all samples equally, which tend to learn discriminative features from simple samples.
Yifan Shi, Kaixiang Yang, Mengzhi Wang, Zhiwen Yu, Huanqiang Zeng, Yang Hu   +5 more
wiley   +1 more source

Graph Generative Models Evaluation with Masked Autoencoder

In recent years, numerous graph generative models (GGMs) have been proposed. However, evaluating these models remains a considerable challenge, primarily due to the difficulty in extracting meaningful graph features that accurately represent real-world graphs.
Chengen Wang 0001, Murat Kantarcioglu
openaire   +3 more sources

SFK: Shape‐ and Function‐Grounded Keypoint Representation for Sequential Manipulation

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Sequential manipulation is the process by which robots perform multiple interdependent steps to accomplish composite tasks, demanding tight integration of perception, planning and execution. Existing methods incorporate explicit features such as category, semantics, 6D pose or affordance to enhance consistency, yet single‐feature ...
Yaxin Liu, Yan Liu, Xinyu Song, Ruiqing Han, Haiyong Liu, Bo Huang, Yufeng Yao, Ming Zhong   +7 more
wiley   +1 more source

Addressing Long‐Tailed Drug–Drug Interactions Through Minimisation of Predictive Uncertainty and Loss Sharpness

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Drug–Drug Interaction (DDI) prediction is critical for ensuring patient safety, particularly under long‐tailed distributions, where frequent (head) interactions dominate whereas rare (tail) interactions remain underrepresented. Conventional loss functions such as cross‐entropy often tend to overfit head classes while they underperform on rare ...
Chao Liu, Xizhao Wang, Farhad Pourpanah, Sam Kwong   +3 more
wiley   +1 more source

Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis. [PDF]

Comput Model Eng Sci, 2023
Zuo Q, Hu J, Zhang Y, Pan J, Jing C, Chen X, Meng X, Hong J.   +7 more
europepmc   +1 more source