Results 71 to 80 of about 649,217 (300)
Knowledge Graph Embeddings [PDF]
, 2012 With the growing popularity of multi-relational data on the Web, knowledge graphs (KGs) have become a key data source in various application domains, such as Web search, question answering, and natural language understanding. In a typical KG such as Freebase (Bollacker et al.
Rosso, Paolo +2 more
openaire +2 more sources
Universal Knowledge Graph Embeddings [PDF]
A variety of knowledge graph embedding approaches have been developed. Most of them obtain embeddings by learning the structure of the knowledge graph within a link prediction setting.
Kouagou, N\u27Dah Jean +6 more
core +1 more source
Rank of divisors on tropical curves [PDF]
, 2013 We investigate, using purely combinatorial methods, structural and algorithmic properties of linear equivalence classes of divisors on tropical curves.
Králʼ, Daniel +2 more
core +1 more source
KDM7A and KDM1A inhibition suppresses tumour promoting pathways in prostate cancer
Molecular Oncology, EarlyView.Treatment resistance is a major challenge for patients with advanced prostate cancer. This study examined an alternative approach to target the major prostate cancer‐promoting pathway by targeting epigenetic factors, whose levels are higher in tumours.
Jennie N Jeyapalan +16 more
wiley +1 more source
Deep Graph Embeddings in Recommender Systems [PDF]
, 2019 Recommender Systems are intelligent machine learning systems that help customers discover a ranked set of personalized products from a dynamic pool of diverse choices.
Soon, Chee Loong
core +2 more sources
Molecular Oncology, EarlyView.This study shows that lung adenocarcinomas exploit developmental branching morphogenesis to acquire a therapy resistant basal‐like tumour cell state. This process was found to be regulated by combined TP53 loss‐of‐function and type‐I interferon signalling, identifying a novel axis for biomarker and therapeutic target discovery.
Kamila J Bienkowska +13 more
wiley +1 more source
Revisiting Embeddings for Graph Neural Networks [PDF]
, 2022 Current graph representation learning techniques use Graph Neural Networks (GNNs) to extract features from dataset embeddings. In this work, we examine the quality of these embeddings and assess how changing them can affect the accuracy of GNNs.
Zhao, A., Purchase, S., Mullins, R. D.
core +1 more source
Resilience in Knowledge Graph Embeddings [PDF]
Transactions on Graph Data and KnowledgeIn recent years, knowledge graphs have gained interest and witnessed widespread applications in various domains, such as information retrieval, question-answering, recommendation systems, amongst others.
Sharma, Arnab +2 more
doaj +1 more source
Co-embedding of edges and nodes with deep graph convolutional neural networks
Scientific Reports, 2023 Graph neural networks (GNNs) have significant advantages in dealing with non-Euclidean data and have been widely used in various fields. However, most of the existing GNN models face two main challenges: (1) Most GNN models built upon the message-passing
Yuchen Zhou +7 more
doaj +1 more source
Molecular Oncology, EarlyView.Combining osimertinib with the STING agonist ADU‐S100 activates innate and adaptive immunity to overcome the non‐inflamed microenvironment of Egfr‐mutant lung cancer. This combination increases NK and CD8+ T‐cell infiltration, associated with activation of the STING‐IRF3 pathway and local immunogenic cell death.
Jun Nishimura +19 more
wiley +1 more source