Results 71 to 80 of about 7,006,651 (299)
Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
wiley +1 more source
Hyperconvexity and endpoints in T₀-quasi-metric spaces [PDF]
, 2013 Over the last decades much progress has been made in the investigation of hyperconvexity in metric spaces. Recently Kemajou and others have published an article concerning hyperconvexity in T₀-quasi-metric spaces.
Haihambo, Paulus
core +1 more source
JUTI: Jurnal Ilmiah Teknologi InformasiWriting can be a powerful and unique medium of self-expression for every individual. Therefore, we propound a deep metric learning technique to acquire the vector representation of text, aiming to enhance the performance of deep learning classification ...
Hendri Darmawan +2 more
doaj +1 more source
Frontiers in Neuroscience, 2020 Metric learning is a class of efficient algorithms for EEG signal classification problem. Usually, metric learning method deals with EEG signals in the single view space. To exploit the diversity and complementariness of different feature representations,
Jing Xue, Xiaoqing Gu, Tongguang Ni
doaj +1 more source
Microbiome‐blood–brain barrier interactions in aging — mechanisms and therapeutic potential
FEBS Letters, EarlyView.Aging reshapes the gut microbiome (↓SCFA‐producing commensals; ↑pro‐inflammatory outputs), shifting circulating metabolites (↓SCFAs; ↑LPS, ↑TMAO, ↑PAA) that act at the BBB to increase nonspecific transcytosis, alter transport, and promote astrocyte reactivity, heightening brain vulnerability.
Daniel Cuervo‐Zanatta +3 more
wiley +1 more source
IEEE Access, 2019 This article presents a concrete mathematical analysis on Information-Theoretic Metric Learning (ITML). The analysis provides a theoretical foundation for ITML, by supplying well-posedness, strong duality, and convergence.
Jooyeon Choi, Chohong Min, Byungjoon Lee
doaj +1 more source
cognitivefactory/features-maximization-metric
, 2023 Release 0.1.0 of cognitivefactory/features-maximization-metric package. GitHub repository : https://github.com/cognitivefactory/features-maximization-metric/tree/0.1.0 Main documentation : https://cognitivefactory.github.io/features-maximization-metric/
Schild, Erwan
core +1 more source
Structure‐forward targeting of claudins with synthetic binders
FEBS Letters, EarlyView.Claudins form the paracellular barriers between epithelial and endothelial tissues at tight junctions and are targets for molecular binders with the goal of modulating barrier permeability. Claudin‐binding molecules are relevant in drug delivery or in altering claudin interactions with disease‐causing proteins.
Alex J. Vecchio
wiley +1 more source
Metric Learning-Based Multi-Instance Multi-Label Classification With Label Correlation
IEEE Access, 2019 In multi-instance multi-label learning (MIML) problems, predicting the labels of unseen bags becomes difficult when the labels of their instances are not provided directly.
Haifeng Hu +3 more
doaj +1 more source
CoRRAppeared in the 41st Conference on Uncertainty in Artificial Intelligence (UAI 2025)
Gokcan Tatli +4 more
openaire +4 more sources