A New Parallel Methodology for the Network Analysis of COVID-19 Data

Agapito, Giuseppe; Milano, Marianna; Cannataro, Mario

doi:10.1007/978-3-030-71593-9_26

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…In this manner, the Kruskal-Wallis test compares the genes with the aim to elucidate statistically similar behaviours among them. Other models [33,34] used the Wilcoxon test [35,36] to compute the SM. To compute the SM, the Wilcoxon test requires that the number of elements in each populations, e.g., the assessed expression levels n for each gene is n ≥ 20 .…”

Section: The Degs Preprocessing Methodsmentioning

confidence: 99%

A statistical network pre-processing method to improve relevance and significance of gene lists in microarray gene expression studies

Agapito

Milano²,

Cannataro³

2022

BMC Bioinformatics

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Background Microarrays can perform large scale studies of differential expressed gene (DEGs) and even single nucleotide polymorphisms (SNPs), thereby screening thousands of genes for single experiment simultaneously. However, DEGs and SNPs are still just as enigmatic as the first sequence of the genome. Because they are independent from the affected biological context. Pathway enrichment analysis (PEA) can overcome this obstacle by linking both DEGs and SNPs to the affected biological pathways and consequently to the underlying biological functions and processes. Results To improve the enrichment analysis results, we present a new statistical network pre-processing method by mapping DEGs and SNPs on a biological network that can improve the relevance and significance of the DEGs or SNPs of interest to incorporate pathway topology information into the PEA. The proposed methodology improves the statistical significance of the PEA analysis in terms of computed p value for each enriched pathways and limit the number of enriched pathways. This helps reduce the number of relevant biological pathways with respect to a non-specific list of genes. Conclusion The proposed method provides two-fold enhancements. Network analysis reveals fewer DEGs, by selecting only relevant DEGs and the detected DEGs improve the enriched pathways’ statistical significance, rather than simply using a general list of genes.

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Section: The Degs Preprocessing Methodsmentioning

confidence: 99%

A statistical network pre-processing method to improve relevance and significance of gene lists in microarray gene expression studies

Agapito

Milano²,

Cannataro³

2022

BMC Bioinformatics

Self Cite

View full text Add to dashboard Cite

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A parallel software pipeline to select relevant genes for pathway enrichment

Agapito

Cannataro

2022

2022 30th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)

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Parallel Network Analysis and Communities Detection (PANC) Pipeline for the Analysis and Visualization of COVID-19 Data

Agapito

Milano

Cannataro

2021

Parallel Process. Lett.

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A new coronavirus, causing a severe acute respiratory syndrome (COVID-19), was started at Wuhan, China, in December 2019. The epidemic has rapidly spread across the world becoming a pandemic that, as of today, has affected more than 70 million people causing over 2 million deaths. To better understand the evolution of spread of the COVID-19 pandemic, we developed PANC (Parallel Network Analysis and Communities Detection), a new parallel preprocessing methodology for network-based analysis and communities detection on Italian COVID-19 data. The goal of the methodology is to analyze set of homogeneous datasets (i.e. COVID-19 data in several regions) using a statistical test to find similar/dissimilar behaviours, mapping such similarity information on a graph and then using community detection algorithm to visualize and analyze the initial dataset. The methodology includes the following steps: (i) a parallel methodology to build similarity matrices that represent similar or dissimilar regions with respect to data; (ii) an effective workload balancing function to improve performance; (iii) the mapping of similarity matrices into networks where nodes represent Italian regions, and edges represent similarity relationships; (iv) the discovering and visualization of communities of regions that show similar behaviour. The methodology is general and can be applied to world-wide data about COVID-19, as well as to all types of data sets in tabular and matrix format. To estimate the scalability with increasing workloads, we analyzed three synthetic COVID-19 datasets with the size of 90.0[Formula: see text]MB, 180.0[Formula: see text]MB, and 360.0[Formula: see text]MB. Experiments was performed on showing the amount of data that can be analyzed in a given amount of time increases almost linearly with the number of computing resources available. Instead, to perform communities detection, we employed the real data set.

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A New Parallel Methodology for the Network Analysis of COVID-19 Data

Cited by 3 publications

References 17 publications

A statistical network pre-processing method to improve relevance and significance of gene lists in microarray gene expression studies

A statistical network pre-processing method to improve relevance and significance of gene lists in microarray gene expression studies

A parallel software pipeline to select relevant genes for pathway enrichment

Parallel Network Analysis and Communities Detection (PANC) Pipeline for the Analysis and Visualization of COVID-19 Data

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