Results 261 to 270 of about 5,876,040 (331)

Reinforcement Learning in Personalized Medicine: A Comprehensive Review of Treatment Optimization Strategies. [PDF]

Cureus
K B, Venkatesan L, Benjamin LS, K V, Satchi NS.   +4 more
europepmc   +1 more source

Blockchain‐Empowered H‐CPS Architecture for Smart Agriculture

Advanced Science, EarlyView.
This article reviews the current system architectures for smart agriculture and proposes a blockchain‐empowered hierarchical cyber‐physical system (H‐CPS) framework. It discusses the challenges and outlines future research directions, including the use of semantic blockchain to handle complex data and models.
Xiaoding Wang, Qibin Wu, Haitao Zeng, Xu Yang, Hui Cui, Xun Yi, Md. Jalil Piran, Ming Luo, Youxiong Que   +8 more
wiley   +1 more source

Multi-wavelength optical information processing with deep reinforcement learning. [PDF]

Light Sci Appl
Yan Q, Ouyang H, Tao Z, Shen M, Du S, Zhang J, Liu H, Hao H, Jiang T.   +8 more
europepmc   +1 more source

Identification and Drug Screening of Single Cells from Human Tumors on Semiconductor Chip for Cancer Precision Medicine

Advanced Science, EarlyView.
The study innovatively integrates single‐cell electric impedance identification and drug screening on a semiconductor chip, addressing tumor heterogeneity limitations. The system achieves label‐free cancer cell recognition while concurrently evaluating drug efficacy on cancer cells and toxicity on noncancerous cells.
Wenhao Hui, Ka‐Meng Lei, Yingying Liu, Xinru Huang, Yunlong Zhong, Xiaojun Chen, Mingji Wei, Jie Yan, Ren Shen, Pui‐In Mak, Rui P. Martins, Shuhong Yi, Ping Wang, Yanwei Jia   +13 more
wiley   +1 more source

Machine Learning‐Enhanced Nanoparticle Design for Precision Cancer Drug Delivery

Advanced Science, EarlyView.
Machine Learning (ML) is revolutionizing cancer nanomedicine by optimizing nanoparticle (NP) design and drug delivery. This review summarizes ML applications across all stages of NP drug delivery, along with a discussion of ongoing challenges and future directions.
Qingquan Wang, Yujian Liu, Chenchen Li, Bin Xu, Shidang Xu, Bin Liu   +5 more
wiley   +1 more source

Activity cliff-aware reinforcement learning for de novo drug design. [PDF]

J Cheminform
Hu X, Liu G, Zhao Y, Zhang H.
europepmc   +1 more source

Karlsruhe Brainstormers - A Reinforcement Learning approach to robotic soccer

, 2001
Martin Riedmiller, A. Merke, David Meier, Andreas Hoffmann, Alex Sinner, O. Thate, R. Ehrmann   +6 more
openalex   +1 more source

Protected Chaos in a Topological Lattice

Advanced Science, EarlyView.
Topological and chaotic dynamics are often considered incompatible, with one expected to dominate or disrupt the other. This work reveals that topological localization can persist even under strong chaotic dynamics and, counter‐intuitively, protect chaotic behavior.
Haydar Sahin, Hakan Akgün, Zhuo Bin Siu, S. M. Rafi‐Ul‐Islam, Jian Feng Kong, Mansoor B. A. Jalil, Ching Hua Lee   +6 more
wiley   +1 more source

Near real-time online reinforcement learning with synchronous or asynchronous updates. [PDF]

Sci Rep
Radac MB, Chirla DP.
europepmc   +1 more source

Artificial Intelligent‐Enhanced Metabolite Profiling for Intraoperative IDH1 Genotyping in Glioma Using an Orthogonally Responsive SERS Probe

Advanced Science, EarlyView.
This study presents a novel approach for intraoperative identification of the IDH1 genotype in glioma patients using a surface‐enhanced Raman scattering (SERS) probe to measure glutathione and hydrogen peroxide. By integrating AI‐driven deep learning algorithms, the method achieves rapid and accurate genotype differentiation, offering the potential to ...
Hang Yin, Xin Zhang, Zheng Zhao, Chong Cao, Minhua Xu, Suhongrui Zhou, Tian Xuan, Ziyi Jin, Limei Han, Yang Fan, Cong Wang, Xiao Zhu, Ying Mao, Jinhua Yu, Cong Li   +14 more
wiley   +1 more source