Results 271 to 280 of about 741,105 (343)

Inferring Gene Regulatory Networks From Single‐Cell RNA Sequencing Data by Dual‐Role Graph Contrastive Learning

Advanced Science, EarlyView.
RegGAIN is a novel and powerful deep learning framework for inferring gene regulatory networks (GRNs) from single‐cell RNA sequencing data. By integrating self‐supervised contrastive learning with dual‐role gene representations, it consistently outperforms existing methods in both accuracy and robustness.
Qiyuan Guan, Jiating Yu, Jieyi Pan, Fan Yuan, Jiadong Ji, Rusong Zhao, Zhi‐Ping Liu, Bingqiang Liu, Ling‐Yun Wu, Duanchen Sun   +9 more
wiley   +1 more source

Machine learning approach using electrochemical immunosensor data for precise classification of Opisthorchis viverrini infection. [PDF]

Sci Rep
Aye NNS, Daduang S, Tippayawat P, Techasen A, Maraming P, Sithithaworn P, Phatthanakun R, Daduang J.   +7 more
europepmc   +1 more source

AI‐Based D‐Amino Acid Substitution for Optimizing Antimicrobial Peptides to Treat Multidrug‐Resistant Bacterial Infection

Advanced Science, EarlyView.
This study constructed the first D‐amino acid antimicrobial peptide dataset and developed an AI model for efficient screening of substitution sites, with 80% of candidate peptides showing enhanced activity. The lead peptide dR2‐1 demonstrated potent antimicrobial activity in vitro and in vivo, high stability, and low toxicity.
Yinuo Zhao, Qingzhou Kong, Haifan Gong, Lixiang Li, Jialu Fu, Boyao Wan, Peizhu Wang, Xiaojuan Li, Yue Wang, Jinghui Zhang, Yanbo Yu, Xiaoyun Yang, Xiuli Zuo, Haina Wang, Yanqing Li   +14 more
wiley   +1 more source

Automated Neuromuscular Assessment: Machine-Learning-Based Facial Palsy Classification Using Surface Electromyography. [PDF]

Sensors (Basel)
Manzoor I, Popescu A, Ricchizzi S, Spolaore A, Gorbachuk M, Tatagiba M, Naros G, Machetanz K.   +7 more
europepmc   +1 more source

Predicting House Prices: A Machine Learning Exploration with Decision Trees and Random Forests

Zhufang Shen
openalex   +1 more source

Biomechanics‐Driven 3D Architecture Inference from Histology Using CellSqueeze3D

Advanced Science, EarlyView.
CellSqueeze3D reconstructs 3D cellular architecture from standard 2D histology images using biomechanical constraints and optimization. Validated on clinical datasets, it enables accurate tissue phenotyping, predicts gene mutations, and reveals significant correlations between nuclear‐cytoplasmic ratio entropy and tumor progression.
Yan Kong, Hui Lu
wiley   +1 more source

Hierarchical Embedded Sphere Model: An Interpretable ML‐Guided Multiscale Descriptor Engineering Decodes OER Activity on TM@MO2 Catalysts

Advanced Science, EarlyView.
Hierarchical Embedded Sphere Model combines DFT and interpretable machine learning to decode catalytic activity on TM‐doped MO2. It disentangles global electronic, active‐site, and local coordination effects, revealing two activation mechanisms: dopant‐driven (Rh@MO2) and coordination‐mediated (Fe@ZrO2).
Ziyuan Li, Shan Gao, Yunhan Wang, Yueyu Zhang, Weichao Wang, Xiangmei Duan   +5 more
wiley   +1 more source

Empowering Flexible Electronics with Piezoelectric Nanogenerators: Breakthroughs from Energy Harvesting to Intelligent Sensing

Advanced Science, EarlyView.
This review systematically summarizes recent advances in self‐powered flexible sensing and energy storage systems based on piezoelectric nanogenerators (PENGs). It highlights their fundamental mechanisms, structural designs, and applications in health monitoring, e‐skin, and tactile sensing. The integration of PENGs with supercapacitors or batteries is
Wu‐Lin Xin, He‐Qing Cai, Xi Cui, Lu Han, Kou Zhang, Xin‐Yu Xue, Yi‐Fei Song, Juan Liu, Zhou Li   +8 more
wiley   +1 more source

Prediction of the Unconfined Compressive Strength of One-Part Geopolymer-Stabilized Soil Under Acidic Erosion: Comparison of Multiple Machine Learning Models. [PDF]

Materials (Basel)
Zhang J, Hu G, Zhang J, Wu J.
europepmc   +1 more source

SAGE: Spatially Aware Gene Selection and Dual‐View Embedding Fusion for Domain Identification in Spatial Transcriptomics

Advanced Science, EarlyView.
SAGE is a unified framework for spatial domain identification in spatial transcriptomics that jointly models tissue architecture and gene programs. Topic‐driven gene selection (NMF plus classifier‐based scoring) highlights spatially informative genes, while dual‐view graph embedding fuses local expression and non‐local functional relations.
Yi He, Yunpei Xu, Liqing Ding, Hong‐Dong Li, Yaohang Li, Shaokai Wang   +5 more
wiley   +1 more source