Results 151 to 160 of about 30,563 (303)
Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space
, 2022 The trends of open science have enabled several open scholarly datasets which include millions of papers and authors. Managing, exploring, and utilizing such large and complicated datasets effectively are challenging. In recent years, the knowledge graph
Takasu, Atsuhiro, Tran, Hung Nghiep
core
Advanced Science, EarlyView.We propose the Full‐Body AI Agent, a multi‐scale collaborative framework with 7 biological‐layer agents. It unifies multi‐omics/clinical data via standardized protocols, enabling phenotype‐guided closed‐loop reasoning, quantitative evaluation, and LLM safeguards, with promising applications in tumor metastasis modeling and precision drug development ...
Aoqi Wang +11 more
wiley +1 more source
Knowledge Graph Embedding for Hierarchical Entities Based on Auto-Embedding Size
MathematicsKnowledge graph embedding represents entities and relations as low-dimensional continuous vectors. Recently, researchers have attempted to leverage the potential semantic connections between entities with hierarchical relationships in the knowledge graph.
Pengfei Zhang +6 more
doaj +1 more source
Counterfactual Reasoning with Knowledge Graph Embeddings
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)Knowledge graph embeddings (KGEs) were originally developed to infer true but missing facts in incomplete knowledge repositories. In this paper, we link knowledge graph completion and counterfactual reasoning via our new task CFKGR. We model the original world state as a knowledge graph, hypothetical scenarios as edges added to the graph, and plausible
Zellinger, Lena +2 more
openaire +3 more sources
Embedding Logical Queries on Knowledge Graphs
, 2018 Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and ...
William L. Hamilton +4 more
openaire +3 more sources
An Integrated NLP‐ML Framework for Property Prediction and Design of Steels
Advanced Science, EarlyView.This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju +5 more
wiley +1 more source
Convolutional Models with Multi-Feature Fusion for Effective Link Prediction in Knowledge Graph Embedding. [PDF]
Entropy (Basel), 2023 Guo Q, Liao Y, Li Z, Lin H, Liang S.
europepmc +1 more source
Advanced Science, EarlyView.This study identifies ARID3A as a key immunosuppressive transcription factor in TNBC. Its inhibition activates the type I IFN pathway, boosting CD8+ T cell infiltration and sensitizing tumors to anti‐PD‐1. The FDA‐approved migraine drug Rimegepant targets ARID3A, enhances immunotherapy efficacy in preclinical models, and establishes a druggable axis to
Teng Zhou +12 more
wiley +1 more source
Advancing drug-target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining. [PDF]
BMC Bioinformatics, 2023 Djeddi WE +3 more
europepmc +1 more source
Advanced Science, EarlyView.The 10B‐enriched monocarbonyl analog of curcumin (BMAC) 10B‐9 enables site‐specific Boron Neutron Capture Therapy (BNCT) on amyloid‐β (Aβ) fibrils. Neutron irradiation induces histidine oxidation and fibril destabilization, as revealed by 1H‐NMR and FESEM analyses.
Sebastiano Micocci +13 more
wiley +1 more source