Notes on the Localization of Generalized Hexagonal Cellular Networks

Azeem, Muhammad; Jamil, Muhammad Kamran; Shang, Yilun

doi:10.3390/math11040844

Cited by 59 publications

(26 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main challenge in the medical knowledge fusion phase is to achieve accurate entity linkage. The possible causes are that the diversity of medical knowledge sources leads to serious multi-source referencing problems of medical entities in different data sources [30][31][32]. Therefore, how to link the extracted entities accurately and correctly to the medical knowledge base in a context-constrained manner is a common concern in the current academic community; (b) Storage method for the knowledge graph.…”

Section: Discussionmentioning

confidence: 99%

Research on Hierarchical Knowledge Graphs of Data, Information, and Knowledge Based on Multiple Data Sources

Liu

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

In the existing medical knowledge graphs, there are problems concerning inadequate knowledge discovery strategies and the use of single sources of medical data. Therefore, this paper proposed a research method for multi-data-source medical knowledge graphs based on the data, information, knowledge, and wisdom (DIKW) system to address these issues. Firstly, a reliable data source selection strategy was used to assign priorities to the data sources. Secondly, a two-step data fusion strategy was developed to effectively fuse the processed medical data, which is conducive to improving the quality of medical knowledge graphs. The proposed research method is for the design of a multi-data-source medical knowledge graph based on the DIKW system. The method was used to design a set of DIK three-layer knowledge graph architectures according to the DIKW system in line with the medical knowledge discovery strategy, employing a scientific method for expanding and updating knowledge at each level of the knowledge graph. Finally, question and answer experiments were used to compare the two different ways of constructing knowledge graphs, validating the effectiveness of the two-step data fusion strategy and the DIK three-layer knowledge graph.

show abstract

Section: Discussionmentioning

confidence: 99%

Research on Hierarchical Knowledge Graphs of Data, Information, and Knowledge Based on Multiple Data Sources

Liu

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…However, it might not always be the optimal solution for capturing rare events [ 28 ]. Other methods such as REWKLR [ 26 ] and hexagonal cellular networks [ 29 ] consider the rarity of events in classification. We used the Gaussian radial basis function (RBF), a commonly used kernel with SVM, to train and predict our data.…”

Section: Methodsmentioning

confidence: 99%

Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification

Al-Aamri

Kudlicki

Maalouf

et al. 2023

Biology

View full text Add to dashboard Cite

Gene expression profiling is one of the most recognized techniques for inferring gene regulators and their potential targets in gene regulatory networks (GRN). The purpose of this study is to build a regulatory network for the budding yeast Saccharomyces cerevisiae genome by incorporating the use of RNA-seq and microarray data represented by a wide range of experimental conditions. We introduce a pipeline for data analysis, data preparation, and training models. Several kernel classification models; including one-class, two-class, and rare event classification methods, are used to categorize genes. We test the impact of the normalization techniques on the overall performance of RNA-seq. Our findings provide new insights into the interactions between genes in the yeast regulatory network. The conclusions of our study have significant importance since they highlight the effectiveness of classification and its contribution towards enhancing the present comprehension of the yeast regulatory network. When assessed, our pipeline demonstrates strong performance across different statistical metrics, such as a 99% recall rate and a 98% AUC score.

show abstract

“…The graphs are pairs of sets of nodes and edges, where nodes represent objects and edges represent relations between pairs of nodes. The flexibility of the data structure enables the wide use of graphs in a wide range of applications, from solving universal network-related problems [ 2 , 3 , 4 ] to molecular structure analysis [ 5 ] and machine learning models [ 6 ]. A special type of graph, containing semantic relations between the entities is known as a knowledge graph [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

BioGraph: Data Model for Linking and Querying Diverse Biological Metadata

Veljković

Orlov

Mitić

2023

IJMS

View full text Add to dashboard Cite

Studying the association of gene function, diseases, and regulatory gene network reconstruction demands data compatibility. Data from different databases follow distinct schemas and are accessible in heterogenic ways. Although the experiments differ, data may still be related to the same biological entities. Some entities may not be strictly biological, such as geolocations of habitats or paper references, but they provide a broader context for other entities. The same entities from different datasets can share similar properties, which may or may not be found within other datasets. Joint, simultaneous data fetching from multiple data sources is complicated for the end-user or, in many cases, unsupported and inefficient due to differences in data structures and ways of accessing the data. We propose BioGraph—a new model that enables connecting and retrieving information from the linked biological data that originated from diverse datasets. We have tested the model on metadata collected from five diverse public datasets and successfully constructed a knowledge graph containing more than 17 million model objects, of which 2.5 million are individual biological entity objects. The model enables the selection of complex patterns and retrieval of matched results that can be discovered only by joining the data from multiple sources.

show abstract

Notes on the Localization of Generalized Hexagonal Cellular Networks

Cited by 59 publications

References 47 publications

Research on Hierarchical Knowledge Graphs of Data, Information, and Knowledge Based on Multiple Data Sources

Research on Hierarchical Knowledge Graphs of Data, Information, and Knowledge Based on Multiple Data Sources

Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification

BioGraph: Data Model for Linking and Querying Diverse Biological Metadata

Contact Info

Product

Resources

About