Integrative Analysis of Multi-Omics and Genetic Approaches—A New Level in Atherosclerotic Cardiovascular Risk Prediction

Genetics and environmental and lifestyle factors deeply affect cardiovascular diseases, with atherosclerosis as the etiopathological factor (ACVD) and their early recognition can significantly contribute to an efficient prevention and treatment of the disease. Due to the vast number of these factors...

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Autores principales:	EIena I. Usova, Asiiat S. Alieva, Alexey N. Yakovlev, Madina S. Alieva, Alexey A. Prokhorikhin, Alexandra O. Konradi, Evgeny V. Shlyakhto, Paolo Magni, Alberico L. Catapano, Andrea Baragetti
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	multi-omics risk prediction cardiovascular disease precision medicine Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/da9b95b75076484399f2165d80420999
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spelling	oai:doaj.org-article:da9b95b75076484399f2165d804209992021-11-25T16:52:38ZIntegrative Analysis of Multi-Omics and Genetic Approaches—A New Level in Atherosclerotic Cardiovascular Risk Prediction10.3390/biom111115972218-273Xhttps://doaj.org/article/da9b95b75076484399f2165d804209992021-10-01T00:00:00Zhttps://www.mdpi.com/2218-273X/11/11/1597https://doaj.org/toc/2218-273XGenetics and environmental and lifestyle factors deeply affect cardiovascular diseases, with atherosclerosis as the etiopathological factor (ACVD) and their early recognition can significantly contribute to an efficient prevention and treatment of the disease. Due to the vast number of these factors, only the novel “omic” approaches are surmised. In addition to genomics, which extended the effective therapeutic potential for complex and rarer diseases, the use of “omics” presents a step-forward that can be harnessed for more accurate ACVD prediction and risk assessment in larger populations. The analysis of these data by artificial intelligence (AI)/machine learning (ML) strategies makes is possible to decipher the large amount of data that derives from such techniques, in order to provide an unbiased assessment of pathophysiological correlations and to develop a better understanding of the molecular background of ACVD. The predictive models implementing data from these “omics”, are based on consolidated AI best practices for classical ML and deep learning paradigms that employ methods (e.g., Integrative Network Fusion method, using an AI/ML supervised strategy and cross-validation) to validate the reproducibility of the results. Here, we highlight the proposed integrated approach for the prediction and diagnosis of ACVD with the presentation of the key elements of a joint scientific project of the University of Milan and the Almazov National Medical Research Centre.EIena I. UsovaAsiiat S. AlievaAlexey N. YakovlevMadina S. AlievaAlexey A. ProkhorikhinAlexandra O. KonradiEvgeny V. ShlyakhtoPaolo MagniAlberico L. CatapanoAndrea BaragettiMDPI AGarticlemulti-omicsrisk predictioncardiovascular diseaseprecision medicineMicrobiologyQR1-502ENBiomolecules, Vol 11, Iss 1597, p 1597 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multi-omics risk prediction cardiovascular disease precision medicine Microbiology QR1-502
spellingShingle	multi-omics risk prediction cardiovascular disease precision medicine Microbiology QR1-502 EIena I. Usova Asiiat S. Alieva Alexey N. Yakovlev Madina S. Alieva Alexey A. Prokhorikhin Alexandra O. Konradi Evgeny V. Shlyakhto Paolo Magni Alberico L. Catapano Andrea Baragetti Integrative Analysis of Multi-Omics and Genetic Approaches—A New Level in Atherosclerotic Cardiovascular Risk Prediction
description	Genetics and environmental and lifestyle factors deeply affect cardiovascular diseases, with atherosclerosis as the etiopathological factor (ACVD) and their early recognition can significantly contribute to an efficient prevention and treatment of the disease. Due to the vast number of these factors, only the novel “omic” approaches are surmised. In addition to genomics, which extended the effective therapeutic potential for complex and rarer diseases, the use of “omics” presents a step-forward that can be harnessed for more accurate ACVD prediction and risk assessment in larger populations. The analysis of these data by artificial intelligence (AI)/machine learning (ML) strategies makes is possible to decipher the large amount of data that derives from such techniques, in order to provide an unbiased assessment of pathophysiological correlations and to develop a better understanding of the molecular background of ACVD. The predictive models implementing data from these “omics”, are based on consolidated AI best practices for classical ML and deep learning paradigms that employ methods (e.g., Integrative Network Fusion method, using an AI/ML supervised strategy and cross-validation) to validate the reproducibility of the results. Here, we highlight the proposed integrated approach for the prediction and diagnosis of ACVD with the presentation of the key elements of a joint scientific project of the University of Milan and the Almazov National Medical Research Centre.
format	article
author	EIena I. Usova Asiiat S. Alieva Alexey N. Yakovlev Madina S. Alieva Alexey A. Prokhorikhin Alexandra O. Konradi Evgeny V. Shlyakhto Paolo Magni Alberico L. Catapano Andrea Baragetti
author_facet	EIena I. Usova Asiiat S. Alieva Alexey N. Yakovlev Madina S. Alieva Alexey A. Prokhorikhin Alexandra O. Konradi Evgeny V. Shlyakhto Paolo Magni Alberico L. Catapano Andrea Baragetti
author_sort	EIena I. Usova
title	Integrative Analysis of Multi-Omics and Genetic Approaches—A New Level in Atherosclerotic Cardiovascular Risk Prediction
title_short	Integrative Analysis of Multi-Omics and Genetic Approaches—A New Level in Atherosclerotic Cardiovascular Risk Prediction
title_full	Integrative Analysis of Multi-Omics and Genetic Approaches—A New Level in Atherosclerotic Cardiovascular Risk Prediction
title_fullStr	Integrative Analysis of Multi-Omics and Genetic Approaches—A New Level in Atherosclerotic Cardiovascular Risk Prediction
title_full_unstemmed	Integrative Analysis of Multi-Omics and Genetic Approaches—A New Level in Atherosclerotic Cardiovascular Risk Prediction
title_sort	integrative analysis of multi-omics and genetic approaches—a new level in atherosclerotic cardiovascular risk prediction
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/da9b95b75076484399f2165d80420999
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Integrative Analysis of Multi-Omics and Genetic Approaches—A New Level in Atherosclerotic Cardiovascular Risk Prediction

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