Node-based Knowledge Graph Contrastive Learning for Medical Relationship Prediction [PDF]
arXiv, 2023 The embedding of Biomedical Knowledge Graphs (BKGs) generates robust representations, valuable for a variety of artificial intelligence applications, including predicting drug combinations and reasoning disease-drug relationships. Meanwhile, contrastive learning (CL) is widely employed to enhance the distinctiveness of these representations.
arxiv
In vitro antiplasmodial activity of1,1 0-phenanthroline derivatives and its quantitative structure-activity relationship [PDF]
, 2003 Mustofa, Ange Desire Yapi, Alexis Valentin, lqmal Tahir - In vitro antiplasmodial activity of 1,10- phenanthroline derivatives and its quantitative structure-activity relationship Background: Previous study showed that 1,10-phenanthroline skeleton were ...
Mustofa, Ange Desire Yapi, Alexis Valentin, lqmal Tahir
core +1 more source
Quantitative Structure-Activity Relationship Modeling and Bayesian Networks: Optimality of Naive Bayes Model [PDF]
, 2019 Previously, computational drag design was usually based on simplified laws of molecular physics, used for calculation of ligand’s interaction with an active site of a protein-enzyme.
Kupervasser, Oleg
core +1 more source
arXiv, 2023 Understanding how genes interact and relate to each other is a fundamental question in biology. However, current practices for describing these relationships, such as drawing diagrams or graphs in a somewhat arbitrary manner, limit our ability to integrate various aspects of the gene functions and view the genome holistically.
arxiv
Quantitative structure-activity relationship models with receptor-dependent descriptors for predicting peroxisome proliferator-activated receptor activities of thiazolidinedione and oxazolidinedione derivatives [PDF]
, 2009 A quantitative structure–activity relationship study has been carried out, in which the relationship between the peroxisome proliferator-activated receptor a and the peroxisome proliferator activated receptor c agonistic activities of thiazo ...
Fernandes, Miguel X. +2 more
core +1 more source
QSAR analyses of Octahydroquinazolinone for insecticidal activity against spodoptera litura and its in-silico validation using molecular Docking study. [PDF]
, 2017 In order to establish a quantitative structure-activity relationship (QSAR) for Insecticide activity against spodoptera litura, we have, first, studied a series of 14 substituted Octahydroquinazolinone and derivatives by using Density functional theory ...
Benlyass, M. +5 more
core +2 more sources
The Traveling Salesman Theorem for Jordan Curves in Hilbert Space [PDF]
arXiv, 2021 Given a metric space $X$, an Analyst's Traveling Salesman Theorem for $X$ gives a quantitative relationship between the length of a shortest curve containing any subset $E\subseteq X$ and a multi-scale sum measuring the ``flatness'' of $E$. The first such theorem was proven by Jones for $X = \mathbb{R}^2$ and extended to $X = \mathbb{R}^n$ by Okikiolu,
arxiv
Structure-activity relationships based on 3D-QSAR CoMFA/CoMSIA and design of aryloxypropanol-amine agonists with selectivity for the human β3-adrenergic receptor and anti-obesity and anti-diabetic profiles [PDF]
, 2018 Indexación: Scopus.Acknowledgments: This work was supported by FONDECYT No. 11130701. We would also like to thank fDoTr CthLeafbr efeora vthaeil afrbeilei tayvoafiltahbeilsitoyf towfa trheer seoqfutwireadret orecqaulciureladt etothcealAcuDla(thet ttph:e/
Andrades-Lagos, J. +7 more
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Visual Social Relationship Recognition [PDF]
arXiv, 2018 Social relationships form the basis of social structure of humans. Developing computational models to understand social relationships from visual data is essential for building intelligent machines that can better interact with humans in a social environment. In this work, we study the problem of visual social relationship recognition in images.
arxiv
A unified theory of grain growth in polycrystalline materials [PDF]
, 2018 Grain growth is a ubiquitous and fundamental phenomenon observed in the cellular structures with the grain assembly separated by a network of grain boundaries, including metals and ceramics. However, the underlying mechanism of grain growth has remained ambiguous for more than 60 years.
arxiv +1 more source