Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Ngo, Nhat"'
Latent space optimization (LSO) is a powerful method for designing discrete, high-dimensional biological sequences that maximize expensive black-box functions, such as wet lab experiments. This is accomplished by learning a latent space from availabl
Externí odkaz:
http://arxiv.org/abs/2411.11265
Unsupervised pre-training on vast amounts of graph data is critical in real-world applications wherein labeled data is limited, such as molecule properties prediction or materials science. Existing approaches pre-train models for specific graph domai
Externí odkaz:
http://arxiv.org/abs/2409.19117
Autor:
Trang, Thuan, Ngo, Nhat Khang, Levy, Daniel, Vo, Thieu N., Ravanbakhsh, Siamak, Hy, Truong Son
Triangular meshes are widely used to represent three-dimensional objects. As a result, many recent works have address the need for geometric deep learning on 3D mesh. However, we observe that the complexities in many of these architectures does not t
Externí odkaz:
http://arxiv.org/abs/2402.04821
Autor:
Ngo, Nhat Khang, Hy, Truong Son
Without knowledge of specific pockets, generating ligands based on the global structure of a protein target plays a crucial role in drug discovery as it helps reduce the search space for potential drug-like candidates in the pipeline. However, contem
Externí odkaz:
http://arxiv.org/abs/2309.16685
Latent representations of drugs and their targets produced by contemporary graph autoencoder models have proved useful in predicting many types of node-pair interactions on large networks, including drug-drug, drug-target, and target-target interacti
Externí odkaz:
http://arxiv.org/abs/2302.08680
Contemporary graph learning algorithms are not well-defined for large molecules since they do not consider the hierarchical interactions among the atoms, which are essential to determine the molecular properties of macromolecules. In this work, we pr
Externí odkaz:
http://arxiv.org/abs/2302.08647
Autor:
Nguyen, Minh Hai1 (AUTHOR), Ngo, Nhat Minh1 (AUTHOR), Kim, Byung‐Kook2 (AUTHOR), Park, Sangbaek1 (AUTHOR) sb.park@cnu.ac.kr
Publikováno v:
Advanced Science. 11/20/2024, Vol. 11 Issue 43, p1-14. 14p.
Latent representations of drugs and their targets produced by contemporary graph autoencoder-based models have proved useful in predicting many types of node-pair interactions on large networks, including drug-drug, drug-target, and target-target int
Externí odkaz:
http://arxiv.org/abs/2209.09941
Autor:
Qin, Jie, Yuan, Shuaihang, Chen, Jiaxin, Amor, Boulbaba Ben, Fang, Yi, Hoang-Xuan, Nhat, Chu, Chi-Bien, Nguyen-Ngoc, Khoi-Nguyen, Cao, Thien-Tri, Ngo, Nhat-Khang, Huynh, Tuan-Luc, Nguyen, Hai-Dang, Tran, Minh-Triet, Luo, Haoyang, Wang, Jianning, Zhang, Zheng, Xin, Zihao, Wang, Yang, Wang, Feng, Tang, Ying, Chen, Haiqin, Wang, Yan, Zhou, Qunying, Zhang, Ji, Wang, Hongyuan
Sketch-based 3D shape retrieval (SBSR) is an important yet challenging task, which has drawn more and more attention in recent years. Existing approaches address the problem in a restricted setting, without appropriately simulating real application s
Externí odkaz:
http://arxiv.org/abs/2207.04945
Publikováno v:
Ho Chi Minh City Open University Journal of Science - Social Sciences, Vol 13, Iss 2, Pp 97-110 (2023)
Implementing video clips to enhance university students’ reading skills has been popular in Vietnam. This study aimed to explore the effects of employing video clips in reading improvement of English-majored freshmen from their perspectives. What i
Externí odkaz:
https://doaj.org/article/1a89023e56074e9aad0624b5553ea59a