2019
DOI: 10.1371/journal.pone.0226190
|View full text |Cite
|
Sign up to set email alerts
|

Shannon entropy approach reveals relevant genes in Alzheimer’s disease

Abstract: Alzheimer’s disease (AD) is the most common type of dementia and affects millions of people worldwide. Since complex diseases are often the result of combinations of gene interactions, microarray data and gene co-expression analysis can provide tools for addressing complexity. Our study aimed to find groups of interacting genes that are relevant in the development of AD. In this perspective, we implemented a method proposed in a previous work to detect gene communities linked to AD. Our strategy combined co-ex… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
23
0

Year Published

2020
2020
2023
2023

Publication Types

Select...
7

Relationship

5
2

Authors

Journals

citations
Cited by 23 publications
(23 citation statements)
references
References 74 publications
0
23
0
Order By: Relevance
“…For this reason, we plan to explore further the characterization of real-world networks through reconstructed potentials, trying in particular to understand whether the typical patterns found in this analysis are an intrinsic feature of the specified domains. Moreover, we will investigate the possibility to improve our framework by combining the reconstructed potentials with other approaches to complex networks, based on entropy 11 , 45 , 46 and machine learning 47 .…”
Section: Discussionmentioning
confidence: 99%
“…For this reason, we plan to explore further the characterization of real-world networks through reconstructed potentials, trying in particular to understand whether the typical patterns found in this analysis are an intrinsic feature of the specified domains. Moreover, we will investigate the possibility to improve our framework by combining the reconstructed potentials with other approaches to complex networks, based on entropy 11 , 45 , 46 and machine learning 47 .…”
Section: Discussionmentioning
confidence: 99%
“…Among unsupervised DR algorithms the most common method is Principal Component Analysis (PCA). PCA projects a multidimensional set of features onto a low dimensional set of features (i.e., the first few principal components) which are constructed so as to preserve as much of the variance of the original data as possible [68] , [69] .…”
Section: Unsupervised Learningmentioning
confidence: 99%
“…To retain only the strongest gene-to-gene relationships, we estimated the maximum entropy value of the betweenness distribution according to Monaco's works [38,39] and selecting the edges with the lowest divergence. The resulting network was from here on treated as an unweighted graph.…”
Section: Database and Graph Estimationmentioning
confidence: 99%