Results 151 to 160 of about 186,158 (247)

MGM as a Large‐Scale Pretrained Foundation Model for Microbiome Analyses in Diverse Contexts

Advanced Science, EarlyView.
We present the Microbial General Model (MGM), a transformer‐based foundation model pretrained on over 260,000 microbiome samples. MGM learns contextualized microbial representations via self‐supervised language modeling, enabling robust transfer learning, cross‐regional generalization, keystone taxa discovery, and prompt‐guided generation of realistic,
Haohong Zhang, Yuli Zhang, Zixin Kang, Jiayun Xiong, Ronghua Yang, Kang Ning   +5 more
wiley   +1 more source

Natural language processing of gene descriptions for overrepresentation analysis with GeneTEA. [PDF]

Genome Biol
Boyle IA, Aquib NA, Kocak M, Creasi R, Montgomery P, Campbell CD, Dempster JM.   +6 more
europepmc   +1 more source

Learned Conformational Space and Pharmacophore Into Molecular Foundational Model

Advanced Science, EarlyView.
The Ouroboros model introduces two orthogonal modules within a unified framework that independently learn molecular representations and generate chemical structures. This design enables flexible optimization strategies for each module and faithful structure reconstruction without prompts or noise.
Lin Wang, Yifan Wu, Hao Luo, Minglong Liang, Yihang Zhou, Cheng Chen, Chris Liu, Jun Zhang, Yang Zhang   +8 more
wiley   +1 more source

Effects of education level on natural language processing in cardiovascular health communication. [PDF]

Front Public Health
Joseph S, Bhardwaj A, Skariah J, Aggarwal I, Shah V, Harris RA.   +5 more
europepmc   +1 more source

PM10 Impairs CD56dim NK Cell Cytotoxicity via FNBP1 Suppression to Exacerbate Rheumatoid Arthritis: Insights from Multimodal Multi‐Omics

Advanced Science, EarlyView.
This study, through multi‐omics approaches and animal models, revealed that air pollutant PM10 exacerbates the progression of rheumatoid arthritis (RA) by suppressing FNBP1 expression and impairing the cytotoxic function of CD56dim NK cells. The “PM10–FNBP1–NK cells” axis provided novel insights into the environmental pathogenesis of RA and suggested ...
Runhan Zhao, Qinyang Zhang, Yu Jiang, Dagang Tang, Xiao Qu, Jun Zhang, Yi Chen, Weixia Duan, Tao Li, Zhengwei Cai, Yanran Huang, Xiaoji Luo   +11 more
wiley   +1 more source

Sentiment Analysis of Autologous Breast Reconstruction Using Natural Language Processing and Deep Learning. [PDF]

Aesthet Surg J Open Forum
Wang MJ, Fijany AJ, Holan CA, Warikoo G, Martinez-Jorge J.   +4 more
europepmc   +1 more source

Gut–Metabolome–Proteome Interactions in Age‐Related Hearing Loss: Insights from Fecal Microbiota Transplantation and Multi‐Omics Analyses

Advanced Science, EarlyView.
Germ‐free (GF) mice receiving fecal microbiota transplantation (FMT) reveal microbiota‐dependent effects on auditory aging. Integrated metagenomic, metabolomic and proteomic profiling maps gut–inner ear network and highlights 5‐hydroxytryptophan (5‐HTP) as a microbiota‐linked metabolic hub in age‐related hearing loss (ARHL).
Ting Yang, Ziwen Gao, Hui Huang, Chanyuan Zhang, Yiquan Tang, Qianhui Qu, Huabin Li, Jing Ke, Zhiji Chen, Menglong Feng, Hu Zhou, Yilai Shu, Wei Yuan   +12 more
wiley   +1 more source

Application of a Natural Language Processing Framework for Data Extraction From Pathology Reports Across Multiple Cancer Types. [PDF]

J Korean Med Sci
Park P, Choi Y, Han N, Park S, Park YL, Hwang J, Choi KS, Yoo CW, Kim HJ.   +8 more
europepmc   +1 more source

In Situ Lipoprotein‐seeking Dye for in Vivo Real‐Time Imaging of Lipid Dysregulation Diseases

Advanced Science, EarlyView.
This study develops lipoprotein‐seeking NIR‐II dyes that specifically bind circulating lipoproteins. These dyes form ultra‐stable complexes with endogenous lipoprotein, enabling real‐time, high‐contrast imaging of fatty liver and atherosclerotic plaques. Tunable binding kinetics allow customized imaging windows.
Yijing Du, Zetao Dang, Baofeng Xu, Jiajun Xu, Dan Wang, Yuewei Zhang, Shoujun Zhu   +6 more
wiley   +1 more source

Neural network for natural language processing to determine treatment urgency in an ophthalmology emergency department. [PDF]

Br J Ophthalmol
Hillenmayer A, Lofi B, Langhans S, Elhardt C, Wolf A, Wertheimer CM.   +5 more
europepmc   +1 more source