Results 81 to 90 of about 158,629 (258)
Advanced Science, EarlyView.Eligibility flow and real‐world AMD burden in the UKB retinal imaging cohort and TMUEH external‐validation cohort. Overview of the ORBIT‐AMD architecture, integrating retinal representation pretraining, bilateral eye‐graph modeling and concept bottleneck learning to support ordered risk, bilateral context, interpretable lesion concepts, longitudinal ...
Xuehao Cui +3 more
wiley +1 more source
Partial Identification of Latent Correlations with Ordinal Data. [PDF]
Psychometrika, 2023 Moss J, Grønneberg S.
europepmc +1 more source
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 +6 more
wiley +1 more source
Iranian South Medical Journal, 2021 Background: Response variables in most medical and health-related research have an ordinal nature. Conventional modeling methods assume predictor variables to be independent, and consider a large number of samples (n) compared to the number of covariates
Zahra Torkashvand +3 more
doaj
Statistical recommendations for count, binary, and ordinal data in rare disease cross-over trials. [PDF]
Orphanet J Rare Dis, 2023 Geroldinger M +10 more
europepmc +1 more source
NbOx Mott Memristor‐Based Oscillatory P‐trit for Ternary Potts Machine
Advanced Science, EarlyView.A ternary Potts machine utilizing NbOx‐based probabilistic trits (p‐trits) is demonstrated for efficient combinatorial optimization. By leveraging the electro‐thermal switching dynamics of NbOx memristors, this architecture generates three‐state stochastic fluctuations.
Hakseung Rhee +10 more
wiley +1 more source
Polychoric Correlation With Ordinal Data in Nursing Research. [PDF]
Nurs Res, 2022 Kiwanuka F +4 more
europepmc +1 more source
Classification of Ordinal Data
CoRR, 2006 Classification of ordinal data is one of the most important tasks of relation learning. In this thesis a novel framework for ordered classes is proposed. The technique reduces the problem of classifying ordered classes to the standard two-class problem. The introduced method is then mapped into support vector machines and neural networks. Compared with
openaire +2 more sources
Advanced Science, EarlyView.A root‐inspired microneedle array patch uses lattice‐based mechanical interlocking to unite rigid microneedles with a flexible substrate. The interlocked rigid–soft architecture improves interfacial bonding, supports stable adhesion on curved and wet tissues under dynamic motion, and provides a versatile platform for robust biointerfacing and future ...
Jongchan Lee +9 more
wiley +1 more source
Factor Retention Using Machine Learning With Ordinal Data. [PDF]
Appl Psychol Meas, 2022 Goretzko D, Bühner M.
europepmc +1 more source