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A Novel Pathway Network Analytics Method Based on Graph Theory.
Saha, Subrata; Soliman, Ahmed; Rajasekaran, Sanguthevar.
  • Saha S; Irving Medical Center, Columbia University, New York, New York, USA.
  • Soliman A; Department of Computer Science and Engineering, University of Connecticut, Storrs, Connecticut, USA.
  • Rajasekaran S; Department of Computer Science and Engineering, University of Connecticut, Storrs, Connecticut, USA.
J Comput Biol ; 28(11): 1104-1112, 2021 11.
Article in English | MEDLINE | ID: covidwho-1376272
ABSTRACT
A biological pathway is an ordered set of interactions between intracellular molecules having collective activity that impacts cellular function, for example, by controlling metabolite synthesis or by regulating the expression of sets of genes. They play a key role in advanced studies of genomics. However, existing pathway analytics methods are inadequate to extract meaningful biological structure underneath the network of pathways. They also lack automation. Given these circumstances, we have come up with a novel graph theoretic method to analyze disease-related genes through weighted network of biological pathways. The method automatically extracts biological structures, such as clusters of pathways and their relevance, significance of each pathway and gene, and so forth hidden in the complex network. We have demonstrated the effectiveness of the proposed method on a set of genes associated with coronavirus disease 2019.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Algorithms / Computational Biology / Metabolic Networks and Pathways / COVID-19 Limits: Humans Language: English Journal: J Comput Biol Journal subject: Molecular Biology / Medical Informatics Year: 2021 Document Type: Article Affiliation country: Cmb.2021.0257

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Algorithms / Computational Biology / Metabolic Networks and Pathways / COVID-19 Limits: Humans Language: English Journal: J Comput Biol Journal subject: Molecular Biology / Medical Informatics Year: 2021 Document Type: Article Affiliation country: Cmb.2021.0257