Results 191 to 200 of about 1,925,652 (255)

scGEN: Leveraging an Adaptive Weighting Strategy for Challenging Cell Clustering in Single‐Cell RNA‐seq

Advanced Science, EarlyView.
scGEN combines attribute–structure encoding, adaptive hard‐sample weighting, and regularized zero‐inflated negative binomial loss to resolve ambiguous single‐cell states. In the developing human pituitary, refined annotations reveal Pre.Gonado cells at 7 weeks and previously obscured developmental communication networks.
Fang Huang, Dayu Hu, Yaqi Dai, Aoyi Geng, Renxiang Guan, Hao Quan, Brett A. McGregor, Zhihan Wang, Nadeem Khan, Hongsheng Zhang, Jinghua Zhang, Mingyang Xia, Xiaoqiong Gu, Di Che, Junguk Hur, Xiaoyu Cui, Kai Guo   +16 more
wiley   +1 more source

Temperature‐Modulated MEMS Gas Sensor Array Enabled by Physics‐Informed Deep Learning for Thermal‐Runaway Characteristic Gas Discrimination

Advanced Science, EarlyView.
This work presents a 2 × 2 integrated single‐chip MEMS gas sensor array with temperature modulation and deep learning for discriminating thermal‐runaway characteristic gases. The array generates distinct temperature‐modulated response patterns for four characteristic gases (H2, CO, CH4, and CO2), while a HK‐MoE model is developed to achieve robust ...
Qin Luo, Qi Guo, Rongyue Liu, Yilin Li, Haiping Zhu, Wei Zhang, Zhongren Chen, Zhaojun Liu, Xing Cheng   +8 more
wiley   +1 more source

DeepLNM: A Multimodal Model for Predicting Lymph Node Metastasis in Colorectal Cancer

Advanced Science, EarlyView.
Traditional diagnosis may miss positive LNM cases, and pathologists face a workload; therefore, we develop DeepLNM, a multimodal model that predicts lymph node metastasis in colorectal cancer from H&E WSIs, CT images, and text. DeepLNM is robust across internal, external, and prospective cohorts and, in a user study, improves diagnostic accuracy while ...
Zhengyu Zhang, Chengyu Lu, Zhen Wang, Zhijian Cen, Yangshu Zhou, Danyi Li, Qi Xie, Bingbing Li, Shujing Guo, Zhijun Gan, Er‐ran Zhu, Jinbang Li, Aihetaimu Aimaier, Rui Mao, Weihao Qiu, Chenglong Zhao, Ying Tan, Xiaolin Liu, Huiting Ma, Tiansu Geng, Peiyue Wu, Huijun Du, Yu Wang, Yueping Liu, Li Liang   +24 more
wiley   +1 more source

A Facial Foundation Model for Clinical Biomarker Prediction and Real‐World Mobile Deployment

Advanced Science, EarlyView.
MedicalFaceFound is a facial foundation model developed through three‐stage progressive pre‐training, adapting representations from general vision to facial structure and clinical phenotypes. Using a single facial image, it estimates 62 biomarkers across multiple physiological systems and supports downstream risk prediction for cardiovascular ...
Tingfeng Xu, Huixuan Xu, Li Lin, Fei Wang, Yaodong Ding, Shuangqiao Liao, Xingyu Chen, Jichen Wen, Hongqiang Zhao, Jing Hao, Weixiang Zhao, Gong Zhang, Shiguang Shan, Yong Zeng, Hu Han, Minxian Wang   +15 more
wiley   +1 more source

Epigenetic Silencing of CYYR1 Promotes Lung Adenocarcinoma Progression Through TRIM21‐Dependent Regulation of HMMR Stability

Advanced Science, EarlyView.
CYYR1 as an epigenetically dysregulated tumor‐suppressive factor that influences oncoprotein stability through TRIM21‐dependent HMMR regulation and reveals a potential therapeutic vulnerability in CYYR1‐deficient LUAD. The graphical illustration was created using FigDraw (www.figdraw.com).
Fengsheng Dai, Jingyi Tang, Huan Rong, Yu Wang, Xiangyi Zhou, Can Xu, Jianhua Wang, Lili Li, Yue Wu, Fei Deng, Dan Yang, Xiao Yang, Renyan Li, Yongzhong Wu, Dongxi Xiang, Tingxiu Xiang   +15 more
wiley   +1 more source

Integrated Multi‐Omics Reveals Cellular States and Microenvironmental Remodeling in Coexisting DCIS and IDC

Advanced Science, EarlyView.
Coexisting DCIS and IDC samples are profiled using spatial transcriptomics, single‐cell RNA sequencing, and single‐cell DNA sequencing. Integrative multi‐omics analysis reveals distinct malignant epithelial and microenvironmental features between DCIS and IDC, which are further validated using Xenium and multiplex immunohistochemistry.
Ning Zhang, Tong Wan, Siyue Zhang, Yunzhen Jiang, Peng Su, Jixin Liu, Zhitong Chen, Jie Hao, Haoyu Wang, Bing Chen, Wenjing Zhao, Lijuan Wang, Tao Xing, Qihuang Zhang, Ulf Schmitz, Zhiyong Ding, Nicola Crosetto, Bingqiang Liu, Qifeng Yang   +18 more
wiley   +1 more source

Generative AI‐Enabled Discovery and Experimental Demonstration of Ultra‐Broadband Epsilon‐Near‐Zero Perfect Absorbers

Advanced Science, EarlyView.
Researchers developed and applied a generative artificial intelligence (AI) model to optimize epsilon‐near‐zero thin film stacks for ultra‐broadband perfect absorption of light. The fabricated AI‐designed absorber, with a total thickness of only 160 nm, experimentally demonstrated an absorption bandwidth exceeding 1000 nm while maintaining greater than
David Dang, Meena Salib, Quynh Dang, Sudip Gurung, Juan Calixto, Xuguo Zhou, Stuart Love, Massee Akbar, Aleksei Anopchenko, Wilton J.M. Kort‐Kamp, Ho Wai Howard Lee   +10 more
wiley   +1 more source

Disentangling Heterogeneous Molecular Networks for Multi‐Omics‐Driven Cancer Driver Discovery

Advanced Science, EarlyView.
DRIVE integrates PPI topology and pan‐cancer multi‐omics profiles through dual‐view graph disentanglement, contrastive representation learning, and joint optimization. Across six PPI networks, DRIVE outperforms ten baselines and remains robust to structural and annotation perturbations.
Xinjing Gong, Ji Li, Mu Su, Peishen Yu, Te Ma, Ruiyang Zhai, Chenye Zhang, Mengyan Zhang, Yan Zhang   +8 more
wiley   +1 more source

qcGEM: A Graph‐Based Molecular Representation With Quantum Chemistry Awareness

Advanced Science, EarlyView.
In this work, we introduce qcGEM, a quantum‐chemistry‐aware graph‐based embedding of molecules that incorporates physical priors into molecular representation learning. Through a specially designed model architecture to adequately process quantum chemistry information as well as three‐dimensional molecular geometry, qcGEM provides a compact, physics ...
Haoyu Wang, Ziyan Zhang, Swee Hong Ng, Haipeng Gong   +3 more
wiley   +1 more source

PrOMet: Accurate and Robust Metal‐Binding Site and Type Prediction

Advanced Science, EarlyView.
PrOMet is a two‐stage deep learning framework for jointly predicting metal‐binding sites and ion identities from protein structures. Surface candidate sites are first pruned, followed by refinement of the most probable sites and metal types. This hierarchical strategy enables robust and accurate metal prediction for cryo‐EM and AlphaFold‐derived ...
Gangeun Park, Chaok Seok, Hahnbeom Park
wiley   +1 more source