Key element identification in large biological datasets: An MCDM comparative study

Modeling biological systems as networks enables efficient analysis, leveraging graph theory. Centrality measures identify key elements, but combining multiple metrics is necessary for comprehensive understanding. This research utilizes multicriteria decision making (MCDM) to rank key proteins in the...

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Bibliographic Details
Published in2024 International Visualization, Informatics and Technology Conference (IVIT) pp. 153 - 158
Main Authors Moiz, Abdul, Fatima, Ubaida
Format Conference Proceeding
LanguageEnglish
Published IEEE 07.08.2024
Subjects
biological networks
Biological systems
Escherichia coli
Graph theory
Informatics
key proteins
Mathematical models
MCDM
Measurement
Object recognition
Proteins
TOPSIS
VIKOR
Visualization
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DOI10.1109/IVIT62102.2024.10692773

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Summary:Modeling biological systems as networks enables efficient analysis, leveraging graph theory. Centrality measures identify key elements, but combining multiple metrics is necessary for comprehensive understanding. This research utilizes multicriteria decision making (MCDM) to rank key proteins in the Escherichia coli network, considering four key network metrics as multi-attributes: the frequency of direct connections (degree centrality), the extent of intermediary roles (betweenness centrality), the proximity to other nodes (closeness centrality), and the influence based on connections to high-scoring nodes (eigenvector centrality). The main objective is to compare two established MCDM methods, TOPSIS and VIKOR, investigating their differences and similarities in identifying and ranking key proteins Results reveals that both methods exhibit similar performance, with VIKOR showing a slight edge in most cases. Notably, VIKOR's superior performance in identifying top-ranked key proteins suggests its potential for applications in biological networks.
DOI:10.1109/IVIT62102.2024.10692773