Results 41 to 50 of about 36,093 (262)
Topology‐Aware Deep Learning on Higher‐Order Structures for Drug Response Prediction
Advanced Science, EarlyView.We present TopDr, a topology‐aware deep learning framework that encodes both drugs and cell lines as multiscale simplicial complexes, capturing interactions at the 0‐, 1‐, and 2‐simplex levels. By jointly integrating local higher‐order neighborhoods and global topological structures, TopDr generates enriched representations for sensitivity prediction ...
Cong Shen +3 more
wiley +1 more source
Advanced Science, EarlyView.This review explores the convergence of artificial intelligence technologies in modeling drug–drug and drug–target interactions. By evaluating advanced feature engineering, architectural innovations, and learning paradigms reveals shared evolutionary trends and critical challenges, such as cold‐start settings and shortcut learning.
Xin Sun, Tong Wang
wiley +1 more source
Agribusiness, EarlyView.ABSTRACT Innovation is essential for competitiveness in agribusiness facing dynamic environments. This study examines how market orientation, marketing, relational, and social capabilities influence innovation performance. Using data from 751 Spanish firms and a multi‐method approach that integrates Structural Equation Modeling (PLS‐SEM), Necessary ...
Beatriz Corchuelo Martínez‐Azúa +1 more
wiley +1 more source
miSolRNA: A tomato micro RNA relational database
BMC Plant Biology, 2010 Background The economic importance of Solanaceae plant species is well documented and tomato has become a model for functional genomics studies. In plants, important processes are regulated by microRNAs (miRNA).
Fernie Alisdair R +8 more
doaj +1 more source
Semantic query in a relational database using a local ontology construction (Retraction)
South African Journal of Science, 2020 The Editor-in-Chief is retracting the paper entitled ‘Semantic query in a relational database using a local ontology construction’ by Saeed M. Sedighi (corresponding author) and Reza Javidan, published in the November/December 2012 issue (volume 108 ...
Saeed M. Sedighi, Reza Javidan
doaj +1 more source
Journal of Cheminformatics, 2023 Current biological and chemical research is increasingly dependent on the reusability of previously acquired data, which typically come from various sources.
Jakub Galgonek, Jiří Vondrášek
doaj +1 more source
A relation merging technique for relational databases [PDF]
[1992] Eighth International Conference on Data Engineering, 2003 A merging technique for relational schemas consisting of relation-schemes, key dependencies, referential integrity constraints, and null constraints is presented. The author examines the conditions required for using this technique with relational database management systems that provide different mechanisms for maintaining null and referential ...
openaire +2 more sources
GraphRAG for engineering diagrams: ChatP&ID enables LLM interaction with P&IDs
AIChE Journal, EarlyView.Abstract Piping and Instrumentation Diagrams (P&IDs) are central to process engineering workflows, yet extracting information from them remains a tedious and time‐consuming task. This work introduces ChatP&ID, a framework enabling natural‐language interaction with smart P&IDs through Graph Retrieval‐Augmented Generation (GraphRAG), to our knowledge ...
Achmad Anggawirya Alimin +1 more
wiley +1 more source
Working with Legacy Relational Data in A Graph-Based World
International Journal of Population Data Science, 2020 The Next Generation Linkage Management System (NGLMS) was designed around keeping all data in a graph database. However, this constraint, while easily achievable for greenfield projects and/or new data linkage units, may not be easily met where legacy ...
James Farrow
doaj +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source