Background: Biologically data-driven networks have become powerful analytical tools that handle massive,\nheterogeneous datasets generated from biomedical fields. Protein-protein interaction networks can identify the\nmost relevant structures directly tied to biological functions. Functional enrichments can then be performed based\non these structural aspects of gene relationships for the study of channelopathies. Channelopathies refer to a\ncomplex group of disorders resulting from dysfunctional ion channels with distinct polygenic manifestations. This\nstudy presents a semi-automatic workflow using protein-protein interaction networks that can identify the most\nrelevant genes and their biological processes and pathways in channelopathies to better understand their\netiopathogenesis. In addition, the clinical manifestations that are strongly associated with these genes are also\nidentified as the most characteristic in this complex group of diseases.\nResults: In particular, a set of nine representative disease-related genes was detected, these being the most\nsignificant genes in relation to their roles in channelopathies. In this way we attested the implication of some\nvoltage-gated sodium (SCN1A, SCN2A, SCN4A, SCN4B, SCN5A, SCN9A) and potassium (KCNQ2, KCNH2) channels in\ncardiovascular diseases, epilepsies, febrile seizures, headache disorders, neuromuscular, neurodegenerative diseases\nor neurobehavioral manifestations. We also revealed the role of Ankyrin-G (ANK3) in the neurodegenerative and\nneurobehavioral disorders as well as the implication of these genes in other systems, such as the immunological or\nendocrine systems.\nConclusions: This research provides a systems biology approach to extract information from interaction networks\nof gene expression. We show how large-scale computational integration of heterogeneous datasets, PPI network\nanalyses, functional databases and published literature may support the detection and assessment of possible\npotential therapeutic targets in the disease. Applying our workflow makes it feasible to spot the most relevant\ngenes and unknown relationships in channelopathies and shows its potential as a first-step approach to identify\nboth genes and functional interactions in clinical-knowledge scenarios of target diseases.\nMethods: An initial gene pool is previously defined by searching general databases under a specific semantic\nframework. From the resulting interaction network, a subset of genes are identified as the most relevant through\nthe workflow that includes centrality measures and other filtering and enrichment databases.
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