Results 161 to 170 of about 139,994 (273)

Mechanism‐Informed Machine Learning Enables Discovery of Oncolytic Peptides for Cancer Immunotherapy

Advanced Science, EarlyView.
MISPOP integrates ensemble learning with membrane‐active physicochemical priors to identify Dermaseptin‐S9, a natural oncolytic peptide that disrupts tumor membranes, triggers immunogenic cell death, and shows strong antitumor activity. The study illustrates a mechanism‐informed route from peptide sequence data to cancer immunotherapy leads.
Wen Zhang, Shengxin Lu, Guangyong Zheng, Shensuo Li, Hongyu Chen, Mei Hong, Xiangru Zhou, Ruotian Tang, Ye Wu, Weidong Zhang, Dong Lu, Xin Luan   +11 more
wiley   +1 more source

Blockchain-enhanced incentive-compatible mechanisms for multi-agent reinforcement learning systems. [PDF]

Sci Rep
Tian K.
europepmc   +1 more source

A Comprehensive Survey on Multi-Agent Reinforcement Learning for Connected and Automated Vehicles. [PDF]

Sensors (Basel), 2023
Yadav P, Mishra A, Kim S.
europepmc   +1 more source

Diffusion‐Based Generative Model With Scaffold‐Hopping Strategy Yields Highly Potent Bioactive Molecules

Advanced Science, EarlyView.
SMarT‐Diff introduces a multi‐objective generative paradigm that integrates scaffold hopping with structure‐aware scoring to enable controlled exploration beyond the training distribution. The framework consistently balances drug‐likeness, synthesizes accessibility and bioactivity, yielding chemically diverse candidates with enhanced properties.
Yuwei Yang, Xiaoqing Gong, Shukai Gu, Jing Li, Bo Liu, Yanan Tian, Qianqian Zhang, Xiaojun Yao, Huanxiang Liu   +8 more
wiley   +1 more source

A multi-agent reinforcement learning framework for exploring dominant strategies in iterated and evolutionary games. [PDF]

Nat Commun
Su Q, Wang H, Xia Y, Wang L.
europepmc   +1 more source

Toward Energy-Efficient Routing of Multiple AGVs with Multi-Agent Reinforcement Learning. [PDF]

Sensors (Basel), 2023
Ye X, Deng Z, Shi Y, Shen W.
europepmc   +1 more source

SIRT6‐Mediated Deacetylation of ATF3 Promotes Silica‐Induced Lung Fibrosis by Enhancing its Nuclear Import via Binding to Importin α

Advanced Science, EarlyView.
SIRT6‐mediated ATF3 acetylation drives MGARP transcription and mitochondrial dysfunction in macrophages, promoting macrophage senescence and pulmonary fibrosis. Mechanistically, HSP70/Importin α competitively binds to ATF3, modulating its nuclear translocation.
Demin Cheng, Wenxia Bu, Fengxu Wang, Yueyuan Jin, Rongzhu Liu, Rui Zhao, Xuehai Wang, Mengna Jiang, Jinping Shen, Xinhang Cheng, Zuming Chen, Li Zhu, Jinlong Li, Zhenzhong Ge, Shichen Miao, Haotian Xu, Xiaoyu Zhou, Dongming Wang, Xinyuan Zhao   +18 more
wiley   +1 more source

Multi-modal and multi-agent reinforcement learning framework for urban traffic flow prediction and signal control optimization. [PDF]

Sci Rep
Wang R, Zhang J, Wang X, He H, Zou Y.
europepmc   +1 more source

Multi-Agent Reinforcement Learning for Traffic Flow Management of Autonomous Vehicles. [PDF]

Sensors (Basel), 2023
Mushtaq A, Haq IU, Sarwar MA, Khan A, Khalil W, Mughal MA.   +5 more
europepmc   +1 more source

Terahertz Channel Modeling, Estimation and Localization in RIS‐Assisted Systems

Advanced Electronic Materials, EarlyView.
Reconfigurable intelligent surfaces have become a recent intensive research focus. Based on practical applications, channel strategies for RIS‐assisted terahertz wireless communication systems are categorized into three different types: channel modeling, channel estimation, and channel localization.
Hongjing Wang, Weipeng Wang, Xuan Cong, Shiyu Wang, Gao Li, Sen Gong, Lin Huang, Hongxin Zeng, Yaxin Zhang, Ziqiang Yang   +9 more
wiley   +1 more source