Výsledky vyhledávání

Report

Inductive Generative Recommendation via Retrieval-based Speculation

Autor: Ding, Yijie, Hou, Yupeng, Li, Jiacheng, McAuley, Julian

Generative recommendation (GR) is an emerging paradigm that tokenizes items into discrete tokens and learns to autoregressively generate the next tokens as predictions. Although effective, GR models operate in a transductive setting, meaning they can

Externí odkaz: http://arxiv.org/abs/2410.02939

Zobrazit plný text záznamu

Report

A generalizable framework for unlocking missing reactions in genome-scale metabolic networks using deep learning

Autor: Liu, Xiaoyi, Yang, Hongpeng, Ai, Chengwei, Dong, Ruihan, Ding, Yijie, Yuan, Qianqian, Tang, Jijun, Guo, Fei

Incomplete knowledge of metabolic processes hinders the accuracy of GEnome-scale Metabolic models (GEMs), which in turn impedes advancements in systems biology and metabolic engineering. Existing gap-filling methods typically rely on phenotypic data

Externí odkaz: http://arxiv.org/abs/2409.13259

Zobrazit plný text záznamu

Report

Multiple Kronecker RLS fusion-based link propagation for drug-side effect prediction

Autor: Qian, Yuqing, Zheng, Ziyu, Tiwari, Prayag, Ding, Yijie, Zou, Quan

Drug-side effect prediction has become an essential area of research in the field of pharmacology. As the use of medications continues to rise, so does the importance of understanding and mitigating the potential risks associated with them. At presen

Externí odkaz: http://arxiv.org/abs/2407.00105

Zobrazit plný text záznamu

Report

SBSM-Pro: Support Bio-sequence Machine for Proteins

Autor: Wang, Yizheng, Zhai, Yixiao, Ding, Yijie, Zou, Quan

Publikováno v: SCIENCE CHINA Information Sciences , Volume 67, Issue 11: 212106 (2024)

Proteins play a pivotal role in biological systems. The use of machine learning algorithms for protein classification can assist and even guide biological experiments, offering crucial insights for biotechnological applications. We introduce the Supp

Externí odkaz: http://arxiv.org/abs/2308.10275

Zobrazit plný text záznamu

Akademický článek

Therapeutic peptides identification via kernel risk sensitive loss-based k-nearest neighbor model and multi-Laplacian regularization.

Autor: Zhang, Wenyu^1,2 (AUTHOR), Ding, Yijie² (AUTHOR), Wei, Leyi³ (AUTHOR), Guo, Xiaoyi² (AUTHOR) kerry.guoxiaoyi@163.com, Ni, Fengming⁴ (AUTHOR) kerry.guoxiaoyi@163.com

Publikováno v: Briefings in Bioinformatics. Nov2024, Vol. 25 Issue 6, p1-13. 13p.