Results 61 to 70 of about 7,645,086 (295)
Nonlinear Dynamic Field Embedding: On Hyperspectral Scene Visualization [PDF]
, 2012 In many areas of research, complex signals are commonly represented by high dimensional feature vectors. However, high dimensional vectors are difficult to analyze and interpret due to the curse of dimensionality.
Lunga, Dalton, Erosy, Okan
core +1 more source
From wide to deep: dimension lifting network for parameter-efficient knowledge graph embedding [PDF]
Knowledge graph embedding (KGE) that maps entities and relations into vector representations is essential for downstream applications. Conventional KGE methods require high-dimensional representations to learn the complex structure of knowledge graph ...
Zhang, He +6 more
core +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
Knowledge Graph Embedding via Metagraph Learning
, 2021 Knowledge graph embedding aims to represent entities and relations in a continuous feature space while preserving the structure of a knowledge graph.
Joyce Jiyoung Whang +3 more
core +1 more source
Translating whole‐genome doubling into precision medicine in cancer
Molecular Oncology, EarlyView.Whole‐genome doubling creates a WGD‐positive tumor state characterized by persistent chromosomal instability, karyotypic diversification, and cellular stress. These same biological pressures drive aggressive tumor evolution while exposing therapeutic vulnerabilities, providing a rationale for WGD‐informed precision medicine. Whole‐genome doubling (WGD)
Sejung Lee, Junghyeok Lim, Jinhyuk Bhin
wiley +1 more source
Predicting biomedical relationships using the knowledge and graph embedding cascade model.
PLoS ONE, 2019 Advances in machine learning and deep learning methods, together with the increasing availability of large-scale pharmacological, genomic, and chemical datasets, have created opportunities for identifying potentially useful relationships within ...
Xiaomin Liang +5 more
doaj +1 more source
MöbiusE: Knowledge Graph Embedding on Möbius ring
, 2021 In this work, we propose a novel Knowledge Graph Embedding (KGE) strategy, called MöbiusE, in which the entities and relations are embedded to the surface of a Möbius ring.
Xiong, W +4 more
core +1 more source
Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source
Restricting the Spurious Growth of Knowledge Graphs by Using Ontology Graphs
IEEE AccessKnowledge Graphs have demonstrated a real advantage in knowledge representation, leveraging graphs NoSQL structures and schema-less technology, which offers superior comprehension, knowledge representation, interpretation, and reasoning.
Kina Tatchukova, Yanzhen Qu
doaj +1 more source
Croppable Knowledge Graph Embedding
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)Knowledge Graph Embedding (KGE) is a common approach for Knowledge Graphs (KGs) in AI tasks. Embedding dimensions depend on application scenarios. Requiring a new dimension means training a new KGE model from scratch, increasing cost and limiting efficiency and flexibility. In this work, we propose a novel KGE training framework MED.
Yushan Zhu +5 more
openaire +4 more sources