AI Article Synopsis
- Combinatorial chemistry creates extensive chemical libraries with numerous potential drug compounds.
- Structural fragments, or subgraphs, are used for analyzing molecular graphs through graph mining techniques.
- This article discusses frequent subgraph mining, its applications, and recent advancements in graph mining.
Article Abstract
Combinatorial chemistry has generated chemical libraries and databases with a huge number of chemical compounds, which include prospective drugs. Chemical structures of compounds can be molecular graphs, to which a variety of graph-based techniques in computer science, specifically graph mining, can be applied. The most basic way for analyzing molecular graphs is using structural fragments, so-called subgraphs in graph theory. The mainstream technique in graph mining is frequent subgraph mining, by which we can retrieve essential subgraphs in given molecular graphs. In this article we explain the idea and procedure of mining frequent subgraphs from given molecular graphs, raising some real applications, and we describe the recent advances of graph mining.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.drudis.2012.07.016 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Relationship Between Elastic, Chemical, and Thermal Properties of SiO Flint Aggregate.
Molecules
December 2024
Department of Chemistry, Faculty of Science, Cadi Ayyad University, Marrakech 40000, Morocco.
Understanding the relationship between elastic, chemical, and thermal properties is essential for the prevention of the behavior of SiO flint aggregates during their application. In fact, the elastic properties of silica depend on chemical and heat treatment. In order to identify the crystallite sizes for natural SiO before and after chemical treatment samples, Williamson-Hall plots and Scherer's formulas are used.
View Article and Find Full Text PDFIntegration of gene expression and DNA methylation data using MLA-GNN for liver cancer biomarker mining.
Front Genet
December 2024
School of information engineering, Jingdezhen Ceramic University, Jingdezhen, China.
The early symptoms of hepatocellular carcinoma patients are often subtle and easily overlooked. By the time patients exhibit noticeable symptoms, the disease has typically progressed to middle or late stages, missing optimal treatment opportunities. Therefore, discovering biomarkers is essential for elucidating their functions for the early diagnosis and prevention.
View Article and Find Full Text PDFHeterophilous distribution propagation for Graph Neural Networks.
Neural Netw
December 2024
College of Computer Science, Zhejiang University, Hangzhou, 310027, China; Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University, Hangzhou, 310027, China. Electronic address:
Graph Neural Networks (GNNs) have achieved remarkable success in various graph mining tasks by aggregating information from neighborhoods for representation learning. The success relies on the homophily assumption that nearby nodes exhibit similar behaviors, while it may be violated in many real-world graphs. Recently, heterophilous graph neural networks (HeterGNNs) have attracted increasing attention by modifying the neural message passing schema for heterophilous neighborhoods.
View Article and Find Full Text PDFA spatio-temporal graph wavelet neural network (ST-GWNN) for association mining in timely social media data.
Sci Rep
December 2024
Henan University of Engineering, Zhengzhou, 451191, China.
Social media generates vast amounts of spatio-temporal sequential data. However, current methods often ignore the complex spatio-temporal correlations within these data. This oversight makes it difficult to fully capture the dynamic features of the data.
View Article and Find Full Text PDFA universal network strategy for lightspeed computation of entropy-regularized optimal transport.
Neural Netw
December 2024
School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, 100190, China; Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, 100190, China. Electronic address:
Optimal transport (OT) is an effective tool for measuring discrepancies in probability distributions and histograms of features. To reduce its high computational complexity, entropy-regularized OT is proposed, which is computed through Sinkhorn algorithm and can be readily integrated into neural networks. However, each time the parameters of networks are updated, both the value and derivative of OT need to be calculated.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!