CARDIOLOGIC ASPECTS OF NETWORK MEDICINE
DOI:
https://doi.org/10.11603/mie.1996-1960.2018.3.9462Keywords:
network medicine, cardiac diseases, network analysis, interdisciplinary approaches, intracellular signaling, multi «omics» data, molecular networks, bioinformatic resource, knowledge ontologyAbstract
Background. The article provides a brief overview of the main publications on networked medicine and the application of an integrated network analysis of protein interactions in the study of human diseases. Given the functional interdependencies between the molecular components in a human cell, the disease is rarely a consequence of an anomaly in one gene, but reflects a complex anomaly of the intracellular network. New tools of network medicine offer a platform for a systematic study not only of the molecular complexity of a specific disease, leading to the identification of modules and pathways of the disease, but also the molecular relationships between manifestly (pathogenic) phenotypes.
Results. The purpose of the study: summarize the results of using the methodology of network medicine, primarily in cardiology.
It is postulated that, when revealing new disease genes, it is necessary to determine the biological significance of disease-related mutations identified as a result of genome studies as a whole, and complete sequencing of the genome, as well as for detection of targets and biomarkers of complex diseases.
Conclusions. Network medicine and the ontology of knowledge have much in common both in the creation strategy and in the technologies of use. However, the tasks of multidimensional modeling are now preferably performed in the «knowledge ontology» strategy. You can also say about education, where ontological solutions are more popular.
Attention is given to the issues of network interconnection of various cardiac diseases at the molecular and phenotypic level. Many diseases of the heart are examined with the help of complex clinical phenotypes formed as a result of integrative influence on the molecular (interactive), genetic (genomic) and ecological (metabolic) levels.
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