Results 241 to 250 of about 176,047 (281)

Decoding Tattoo and Permanent Makeup Pigments: Linking Physicochemical Properties to Absorption, Distribution, Metabolism, and Elimination Profiles Using Quantitative Structure–Activity Relationship (QSAR)‐Based New Approach Methodologies (NAMs)

Advanced Intelligent Discovery, EarlyView.
This study applies QSAR‐based new approach methodologies to 90 synthetic tattoo and permanent makeup pigments, revealing systemic links between their physicochemical properties and absorption, distribution, metabolism, and elimination profiles. The correlation‐driven analysis using SwissADME, ChemBCPP, and principal component analysis uncovers insights
Girija Bansod, Ajay Vikram Singh, Preeti Bhardwaj, Tulika Rai, Sweta Vijay Nakhale, Amruta Shelar, Rajendra Patil, Peter Laux, Andreas Luch, Christopher J. Osgood, Michael W. Stacey   +10 more
wiley   +1 more source

IFNg_DeepKG: A Novel Model for Identifying Interferon-Gamma-Inducing Epitopes Using Knowledge Graph RAG in Biomedical Applications. [PDF]

J Chem Inf Model
Le VT, Yuune JPT, Ou YY.
europepmc   +1 more source

Knowledge graph-based intelligent data management and information innovation service model for university library systems. [PDF]

PLoS One
Liu F.
europepmc   +1 more source

SynergyGraph: predicting cell line specific drug combination synergy scores using knowledge graph representation and hypergraph modeling. [PDF]

Sci Rep
Mehrabani M, Lakizadeh A, Siahpirani AF, Salimi M, Zare-Mirakabad F, Masoudi-Nejad A.   +5 more
europepmc   +1 more source

ConvAHKG: Action-based hybrid knowledge graph with a dual-channel convolutional approach for drug repurposing. [PDF]

Sci Rep
Khodadadi AghGhaleh M, Abedian R, Zarghami R, Fotuhi Siahpirani A, Gharaghani S.   +4 more
europepmc   +1 more source

Intelligent MDT treatment decision making for stage III NSCLC using dual level embedding and three level explanation. [PDF]

Sci Rep
Chen Z, Chai N, Wang J, Wang X, Fan Z.
europepmc   +1 more source

Transformer-based graphs for drug-drug interaction with chemical knowledge embedding. [PDF]

Brief Bioinform
Zhang J, Zhang X, Dai Y, Shao X, Fan X.
europepmc   +1 more source

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Adversarial Explanations for Knowledge Graph Embeddings

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022
We propose a novel black-box approach for performing adversarial attacks against knowledge graph embedding models. An adversarial attack is a small perturbation of the data at training time to cause model failure at test time. We make use of an efficient rule learning approach and use abductive reasoning to identify triples which are logical ...
Betz, Patrick, Meilicke, Christian, Stuckenschmidt, Heiner   +2 more
openaire   +2 more sources

Capturing Knowledge Graph Embeddings

2020
In this chapter we focus on knowledge graph embeddings, an approach to produce embeddings for concepts and names that are the main nodes in knowledge graphs, as well as the relations between them. The resulting embeddings aim to capture the knowledge encoded in the structure of the graph, in terms of how nodes are related to one another. This technique
Jose Manuel Gomez-Perez, Ronald Denaux, Andres Garcia-Silva   +2 more
openaire   +1 more source

Path-specific knowledge graph embedding

Knowledge-Based Systems, 2018
Abstract Knowledge graph embedding aims to represent entities, relations and multi-step relation paths of a knowledge graph as vectors in low-dimensional vector spaces, and supports many applications, such as entity prediction, relation prediction, etc.
Yantao Jia, Yuanzhuo Wang, Xiaolong Jin, Xueqi Cheng   +3 more
openaire   +1 more source