Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Feng, Zaiwen"'
Autor:
Hu, Junwei, Bewong, Michael, Kwashie, Selasi, Zhang, Wen, Nofong, Vincent M., Wu, Guangsheng, Feng, Zaiwen
Drug-target interaction (DTI) prediction is crucial for drug development and repositioning. Methods using heterogeneous graph neural networks (HGNNs) for DTI prediction have become a promising approach, with attention-based models often achieving exc
Externí odkaz:
http://arxiv.org/abs/2411.00801
Autor:
Hu, Junwei, Bewong, Michael, Kwashie, Selasi, Zhang, Yidi, Nofong, Vincent, Wondoh, John, Feng, Zaiwen
This paper studies the entity resolution (ER) problem in property graphs. ER is the task of identifying and linking different records that refer to the same real-world entity. It is commonly used in data integration, data cleansing, and other applica
Externí odkaz:
http://arxiv.org/abs/2410.04783
Estimating causal effects from observational data is challenging, especially in the presence of latent confounders. Much work has been done on addressing this challenge, but most of the existing research ignores the bias introduced by the post-treatm
Externí odkaz:
http://arxiv.org/abs/2408.07219
Autor:
Cheng, Debo, Xie, Yang, Xu, Ziqi, Li, Jiuyong, Liu, Lin, Liu, Jixue, Zhang, Yinghao, Feng, Zaiwen
In causal inference, it is a fundamental task to estimate the causal effect from observational data. However, latent confounders pose major challenges in causal inference in observational data, for example, confounding bias and M-bias. Recent data-dr
Externí odkaz:
http://arxiv.org/abs/2312.05404
Autor:
Ye, Wenting, Li, Chen, Xie, Yang, Zhang, Wen, Zhang, Hong-Yu, Wang, Bowen, Cheng, Debo, Feng, Zaiwen
Identifying and discovering drug-target interactions(DTIs) are vital steps in drug discovery and development. They play a crucial role in assisting scientists in finding new drugs and accelerating the drug development process. Recently, knowledge gra
Externí odkaz:
http://arxiv.org/abs/2306.00041
Autor:
Zhou, Guangtong, Kwashie, Selasi, Zhang, Yidi, Bewong, Michael, Nofong, Vincent M., Cheng, Debo, He, Keqing, Feng, Zaiwen
This paper studies the discovery of approximate rules in property graphs. We propose a semantically meaningful measure of error for mining graph entity dependencies (GEDs) at almost hold, to tolerate errors and inconsistencies that exist in real-worl
Externí odkaz:
http://arxiv.org/abs/2304.02323
Autor:
Liu, Linyue, Guo, Xi, Ouyang, Chun, Hung, Patrick C. K., Zhang, Hong-Yu, He, Keqing, Mo, Chen, Feng, Zaiwen
With the continuous development of business process management technology, the increasing business process models are usually owned by large enterprises. In large enterprises, different stakeholders may modify the same business process model. In orde
Externí odkaz:
http://arxiv.org/abs/2303.17388
Autor:
Wang, Ying, Jiang, Qin, Geng, Yilin, Hu, Yuren, Tang, Yue, Li, Jixiang, Zhang, Junmei, Mayer, Wolfgang, Liu, Shanmei, Zhang, Hong-Yu, Yan, Xianghua, Feng, Zaiwen
Gut microbiota plays a crucial role in modulating pig development and health, and gut microbiota characteristics are associated with differences in feed efficiency. To answer open questions in feed efficiency analysis, biologists seek to retrieve inf
Externí odkaz:
http://arxiv.org/abs/2302.11314
Autor:
Liu, Dehua, Kwashie, Selasi, Zhang, Yidi, Zhou, Guangtong, Bewong, Michael, Wu, Xiaoying, Guo, Xi, He, Keqing, Feng, Zaiwen
Graph entity dependencies (GEDs) are novel graph constraints, unifying keys and functional dependencies, for property graphs. They have been found useful in many real-world data quality and data management tasks, including fact checking on social med
Externí odkaz:
http://arxiv.org/abs/2301.06264
Causal inference plays an important role in under standing the underlying mechanisation of the data generation process across various domains. It is challenging to estimate the average causal effect and individual causal effects from observational da
Externí odkaz:
http://arxiv.org/abs/2301.01549