Results 211 to 220 of about 1,212,418 (284)

Machine Learning and Mendelian Randomization Identify Allergic Rhinitis as Nasopharyngeal Carcinoma Risk Factor With Validated Potential Candidate Biomarkers. [PDF]

Int J Genomics
Chen M, Yang B, Gong C, Liao X, He K, Li Z.   +5 more
europepmc   +1 more source

A Degradable Bioinspired Flier with Aerogel‐Based Colorimetric Sensors for Environmental Monitoring

Advanced Science, EarlyView.
Biodegradable fliers are developed inspired by Tipuana tipu samaras, integrating cellulose nanocrystal aerogel (CNCa) sensors loaded with natural dyes for pH and ammonia detection. The lightweight, degradable fliers mimic natural morphology and aerodynamics, offering an eco‐friendly, scalable solution for in situ environmental monitoring after passive ...
Gianpaolo Gallo, Ruowen Tu, Carlo Filippeschi, Stefano Mariani, Barbara Mazzolai   +4 more
wiley   +1 more source

Knockoff-ML: a knockoff machine learning framework for controlled variable selection and risk stratification in electronic health record data. [PDF]

NPJ Digit Med
Wang Q, Li L, Yang Y.
europepmc   +1 more source

Immune Predictors of Radiotherapy Outcomes in Cervical Cancer

Advanced Science, EarlyView.
This study reveals dynamic immune remodeling in cervical cancer following radiotherapy. Single‐cell analysis identifies the C3/C3AR1 axis as a central mediator of epithelial–myeloid crosstalk, whose inhibition reduces treatment efficacy in mice. Guided by these insights, the eight‐feature machine‐learning model: Cervical Cancer Radiotherapy Immune ...
Linghao Wang, Jie Zhu, Zequn Ding, Zhiyuan Xie, Xingchen Liu, Feihong Zhang, Xiaojun Liu, Yan Zhang, Haiyan Chen   +8 more
wiley   +1 more source

Evaluating algorithmic fairness of machine learning models in predicting underweight, overweight, and adiposity across socioeconomic and caste groups in India: evidence from the longitudinal ageing study in India. [PDF]

PLOS Digit Health
Lee JT, Hsu SH, Li VC, Anindya K, Chen MH, Wang C, Shen TK, Liu VTN, Chen HH, Atun R.   +9 more
europepmc   +1 more source

Using machine learning and statistics for mapping BTSV boundaries from penetration and softening point requirements

Mohammad Abbas, Mārtiņš Zaumanis, Olga Paperna   +2 more
openalex   +1 more source

Comprehensive Profiling of N6‐methyladnosine (m6A) Readouts Reveals Novel m6A Readers That Regulate Human Embryonic Stem Cell Differentiation

Advanced Science, EarlyView.
This research deciphers the m6A transcriptome by profiling its sites and functional readout effects: from mRNA stability, translation to alternative splicing, across five different cell types. Machine learning model identifies novel m6A‐binding proteins DDX6 and FXR2 and novel m6A reader proteins FUBP3 and L1TD1.
Zhou Huang, Rucong Liu, Zibaguli Wubulikasimu, Wanqing Zhao, Jiaqi Huang, Jiaxuan Wang, Tianyuan Zhang, Rui Fan, Wei Kong, Qinghua Cui, Yang Li, Yuan Zhou   +11 more
wiley   +1 more source

Invasive and non-invasive variables prediction models for cardiovascular disease-specific mortality between machine learning vs. traditional statistics. [PDF]

Sci Rep
Choi S, Oh M, Lee DH, Jee SH, Jeon JY.
europepmc   +1 more source

Application of machine learning algorithms for groundwater level prediction in the Najafabad plain. [PDF]

Sci Rep
Davari S, Eslamian S, Jamali M, Safavi HR.   +3 more
europepmc   +1 more source

Heat treatment-driven optimization of Al7075-AlN composites: A synergistic approach using experiments, statistics, and machine learning

Ravitej Y P, Annapoorna T L, Batluri Tilak Chandra, Rajeev Gupta, Bashir Asdaque, KrantiKumar Ksharad, Ramakumar BVN, Abhijith N, Vishnu Vijay Kumar   +8 more
openalex   +1 more source