Results 161 to 170 of about 184,759 (266)

Combining Missing Data Imputation and Internal Validation in Clinical Risk Prediction Models. [PDF]

Stat Med
Mi J, Tendulkar RD, Sittenfeld SMC, Patil S, Zabor EC.   +4 more
europepmc   +1 more source

Large‐scale Whole‐Exome Sequencing Defines the Protein‐Coding Architecture of Retinal Structure, Visual Function, and Major Blinding Diseases

Advanced Science, EarlyView.
Large‐scale whole‐exome sequencing in 356,982 UK Biobank participants defines the protein‐coding architecture of retinal structure, visual function, and major blinding diseases. Pleiotropic genes, including CFI, C3, and RIOX1, bridge multiple retinal phenotypes, while experimental validation of FYB2 implicates RPE barrier dysfunction, providing ...
Jianqing Li, Yijun Ge, Jingxiao Du, Liu Yang, Bangsheng Wu, Chenyue Hang, Mengxi Shen, Yimin Mao, Ting Zhang, Qiyu Bo, Tianyuan Zhao, Yutian Jiao, Yazhi Wang, Shunxiang Gao, Jieqiong Chen, Junran Sun, Tong Li, Huixun Jia, Yang Dou, Wei Cheng, Xiaoling Wan, Jintai Yu, Xiaodong Sun, Bai Lu   +23 more
wiley   +1 more source

Self-Attention-Based Deep Learning for Missing Sensor Data Imputation in Real-Time Probe Card Monitoring. [PDF]

Sensors (Basel)
Bejani M, Mauri M, Mariani S.
europepmc   +1 more source

A Global Methane Observation System to Reduce Uncertainty for Anthropogenic and Natural Sources and Sinks for Detecting and Attributing Climate Feedbacks

Advanced Science, EarlyView.
Conceptual illustration of a Global Ecosystem Methane Observing System (GEM‐OS) integrating satellites, aircraft, atmospheric networks, and ecosystem measurements to quantify methane emissions from anthropogenic and natural sources. The multi‐scale observing framework improves source attribution, reduces uncertainty in regional methane budgets, and ...
P. Ciais, S. Peng, J. Chang, F. Li, Q. Zhu, K. Yuan, G. Hugelius, H. Li, Y. Cai, F. Chevallier, K. Tibrewal, E. A. Kort, K. Arndt, J. Watts, B. Buma, N. Besic, P. I. Palmer, H. Cadillo‐Quiroz, E. Euskirchen, M. J. Gondwe, A. Hoyt, R. Jackson, S. Malone, D. Monteverde, S. Natali, M. Ramonet, C. Rey‐Sanchez, L. B. Sagang, E. A. G. Schuur, R. Vargas, R. Varner, Z. Zhang, B. Poulter   +32 more
wiley   +1 more source

A self-supervised framework for laboratory data imputation in electronic health records. [PDF]

Commun Med (Lond)
Heilbroner SP, Carter C, Vidmar DM, Mueller ET, Stumpe MC, Miotto R.   +5 more
europepmc   +1 more source

PolyG Fibrils Coalesce Into Nuclear Ribbons That Engage Proteostasis Machinery in Neuronal Intranuclear Inclusion Disease

Advanced Science, EarlyView.
In NIID, expanded NOTCH2NLC repeats give rise to nuclear polyG inclusions. Tracer‐guided in situ cryo‐electron tomography enables cross‐scale structural analysis from mouse brain to native neuronal nuclei, revealing dense‐core/peripheral‐halo inclusions built from compact polyG ribbons.
Hui Dong, Yongcheng Pan, Zhiyao Tang, Yuxuan Yao, Guicong Zhang, Junpu Wang, Haonan Xiao, Yun Tian, Beisha Tang, Dan Li, Qiang Guo, Ruijun Tian, Qiong Liu, Cong Liu   +13 more
wiley   +1 more source

Box-meter integrated solution for power data imputation through device design and deep learning integration. [PDF]

Sci Rep
Gao C, Lin H, Lin Y.
europepmc   +1 more source

Machine Learning–Guided Surface Strain Engineering in Connected Platinum–Nickel Nanoparticle Catalysts for Advanced Oxygen Reduction Performance

Advanced Science, EarlyView.
Machine learning‐guided strain engineering enables highly active, durable, support‐free Pt–Ni nanonetwork catalysts for the oxygen reduction reaction. Analysis of a Pt‐based catalyst dataset identifies surface compressive strain as an effective descriptor associated with enhanced activity and provided practical design guidelines.
Aparna Chitra Sudheer, Gopinathan M. Anilkumar, Hidenori Kuroki, Yuuki Sugawara, Takeo Yamaguchi   +4 more
wiley   +1 more source

Biomimetic model for computing missing data imputation and inconsistency reduction in pairwise comparisons matrices. [PDF]

PLoS One
Koczkodaj WW, Pedrycz W, Pigazzini A, Pigazzini LP.   +3 more
europepmc   +1 more source

Pasta, a Versatile Transcriptomic Clock, Maps the Chemical and Genetic Determinants of Aging and Rejuvenation

Advanced Science, EarlyView.
Pasta is a transcriptomic aging clock built on an age‐shift learning framework and trained on 17 000 samples across 21 datasets. It accurately predicts relative biological age across tissues, platforms, and species, captures stemness‐to‐senescence transitions, and identifies age‐modulatory perturbations.
Jérôme Salignon, Maria Tsiokou, Patricia Marqués, Enriqueta Rodríguez‐Diaz, Hazel Ang, Federico Pietrocola, Christian G. Riedel   +6 more
wiley   +1 more source