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COMET:Combined Matrix for Elucidating Targets

Autor: Wang, Haojie, Zhang, Zhe, Gao, Haotian, Zhang, Xiangying, Chen, Zhihang, Chen, Xinchong, Qi, Yifei, Li, Yan, Wang, Renxiao

Identifying the interaction targets of bioactive compounds is a foundational element for deciphering their pharmacological effects. Target prediction algorithms equip researchers with an effective tool to rapidly scope and explore potential targets.

Externí odkaz: http://arxiv.org/abs/2412.02471

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NeurDB: On the Design and Implementation of an AI-powered Autonomous Database

Autor: Zhao, Zhanhao, Cai, Shaofeng, Gao, Haotian, Pan, Hexiang, Xiang, Siqi, Xing, Naili, Chen, Gang, Ooi, Beng Chin, Shen, Yanyan, Wu, Yuncheng, Zhang, Meihui

Databases are increasingly embracing AI to provide autonomous system optimization and intelligent in-database analytics, aiming to relieve end-user burdens across various industry sectors. Nonetheless, most existing approaches fail to account for the

Externí odkaz: http://arxiv.org/abs/2408.03013

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Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting

Autor: Dong, Zheng, Jiang, Renhe, Gao, Haotian, Liu, Hangchen, Deng, Jinliang, Wen, Qingsong, Song, Xuan

Spatiotemporal time series forecasting plays a key role in a wide range of real-world applications. While significant progress has been made in this area, fully capturing and leveraging spatiotemporal heterogeneity remains a fundamental challenge. Th

Externí odkaz: http://arxiv.org/abs/2405.10800

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Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting

Autor: Gao, Haotian, Jiang, Renhe, Dong, Zheng, Deng, Jinliang, Ma, Yuxin, Song, Xuan

Spatiotemporal forecasting techniques are significant for various domains such as transportation, energy, and weather. Accurate prediction of spatiotemporal series remains challenging due to the complex spatiotemporal heterogeneity. In particular, cu

Externí odkaz: http://arxiv.org/abs/2312.00516

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Akademický článek

Prediction of Drug-Target Interactions Based on Interactive Multi-Feature Fusion Algorithm

Autor: GAO Haotian, LI Dongxi, CHEN Zehua, ZHAO Qian

Publikováno v: Taiyuan Ligong Daxue xuebao, Vol 55, Iss 4, Pp 751-758 (2024)

Purposes The prediction of drug-target interactions plays a crucial role in drug relocation and drug development. Methods A multi-feature fusion algorithm is proposed based on the combination of Redundancy-Correlation and Interaction (RCI), and the p

Externí odkaz: https://doaj.org/article/8439cc9e80414c2abeb933ef1e0384df

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