Results 191 to 200 of about 298,347 (266)

De Novo Multi‐Mechanism Antimicrobial Peptide Design via Multimodal Deep Learning

Advanced Science, EarlyView.
Current AI‐driven peptide discovery often overlooks complex structural data. This study presents M3‐CAD, a generative pipeline that leverages 3D voxel coloring and a massive database of over 12 000 peptides to capture nuanced physicochemical contexts.
Xiaojuan Li, Haifan Gong, Yue Wang, Yinuo Zhao, Lixiang Li, Peijing Bao, Qingzhou Kong, Jialu Fu, Boyao Wan, Yumeng Zhang, Jinghui Zhang, Jiekun Ni, Zhongxue Han, Xueping Nan, Kunping Ju, Longfei Sun, Yuerui Ma, Huijun Chang, Mengqi Zheng, Yanbo Yu, Xiaoyun Yang, Xiuli Zuo, Haina Wang, Yanqing Li   +23 more
wiley   +1 more source

Quantum-enhanced hybrid deep reinforcement learning for real-time volleyball tactical decision making. [PDF]

Sci Rep
Cai N, Zhao M, Ke Y, Liu X.
europepmc   +1 more source

S3RL: Enhancing Spatial Single‐Cell Transcriptomics With Separable Representation Learning

Advanced Science, EarlyView.
Separable Spatial Representation Learning (S3RL) is introduced to enhance the reconstruction of spatial transcriptomic landscapes by disentangling spatial structure and gene expression semantics. By integrating multimodal inputs with graph‐based representation learning and hyperspherical prototype modeling, S3RL enables high‐fidelity spatial domain ...
Laiyi Fu, Penglei Wang, Gaoyuan Xu, Jitao Lu, Qinke Peng, Danyang Wu, Hequan Sun   +6 more
wiley   +1 more source

Deep Reinforcement Learning-Based Intelligent Water Level Control: From Simulation to Embedded Implementation. [PDF]

Sensors (Basel)
Cusihuallpa-Huamanttupa K, Sacoto-Cabrera EJ, Coaquira-Castillo RJ, Utrilla Mego LW, Herrera-Levano JC, Concha-Ramos Y, Moreno-Cardenas E.   +6 more
europepmc   +1 more source

High‐Fidelity Synthetic Data Replicates Clinical Prediction Performance in a Million‐Patient Diabetes Cohort

Advanced Science, EarlyView.
This study generates high‐fidelity synthetic longitudinal records for a million‐patient diabetes cohort, successfully replicating clinical predictive performance. However, deeper analysis reveals algorithmic biases and trajectory inconsistencies that escape standard quality metrics. These findings challenge current validation norms, demonstrating why a
Francisco Ortuño, Víctor M. de la Oliva Roque, Javier‐Ignacio Ramirez‐Lopez, David P. Kreil, Joaquín Dopazo, Carlos Loucera   +5 more
wiley   +1 more source

Deep reinforcement learning for resource allocation and scalable numerology in NR-U enabled multi-RAT HetNets. [PDF]

Sci Rep
Elmosilhy NA, Elmesalawy MM, El-Haleem AMA, Ibrahim II.   +3 more
europepmc   +1 more source

In Situ X‐Ray Tomography and Acoustic Emission Monitoring of Damage Evolution in C/C‐SiC Composites Fabricated by Liquid Silicon Infiltration

Advanced Science, EarlyView.
This study investigates how the internal structure of fiber‐reinforced ceramic composites affects their resistance to damage. By combining 3D X‐ray imaging with acoustic emission monitoring during mechanical testing, it reveals how silicon distribution influences crack formation.
Yang Chen, Renato S. M. Almeida, Stefan Flauder, Guillaume Couégnat, Kamen Tushtev, Jürgen Horvath, Peter Wriggers, Kurosch Rezwan   +7 more
wiley   +1 more source

Enhancing Spectral Efficiency of 6G Downlink Beamforming via Cooperative Multi-Agent Deep Reinforcement Learning. [PDF]

Sensors (Basel)
Al Janaby A, Al-Rizzo H, Qassim Y.
europepmc   +1 more source

Enhancing Deep learning/reinforcement learning

The deliverable will initially detail the Big Data and ML framework that enables MLaaS and supports decentralised federated ML Then it studies different deep ML andor reinforcement learning techniques including selflearning techniques and supervisedunsupervised learning procedures applied to IoT devices to select those that better fit the IoTNGIN ...
openaire   +1 more source