Dataset Information

Machine Learning and Bioinformatics Framework Integration to Potential Familial DCM-Related Markers Discovery.

ABSTRACT:

Objectives

Dilated cardiomyopathy (DCM) is characterized by a specific transcriptome. Since the DCM molecular network is largely unknown, the aim was to identify specific disease-related molecular targets combining an original machine learning (ML) approach with protein-protein interaction network.

Methods

The transcriptomic profiles of human myocardial tissues were investigated integrating an original computational approach, based on the Custom Decision Tree algorithm, in a differential expression bioinformatic framework. Validation was performed by quantitative real-time PCR.

Results

Our preliminary study, using samples from transplanted tissues, allowed the discovery of specific DCM-related genes, including MYH6, NPPA, MT-RNR1 and NEAT1, already known to be involved in cardiomyopathies Interestingly, a combination of these expression profiles with clinical characteristics showed a significant association between NEAT1 and left ventricular end-diastolic diameter (LVEDD) (Rho = 0.73, p = 0.05), according to severity classification (NYHA-class III).

Conclusions

The use of the ML approach was useful to discover preliminary specific genes that could lead to a rapid selection of molecular targets correlated with DCM clinical parameters. For the first time, NEAT1 under-expression was significantly associated with LVEDD in the human heart.

SUBMITTER: Schiano C

PROVIDER: S-EPMC8701745 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Publications

Machine Learning and Bioinformatics Framework Integration to Potential Familial DCM-Related Markers Discovery.

Schiano Concetta C Franzese Monica M Geraci Filippo F Zanfardino Mario M Maiello Ciro C Palmieri Vittorio V Soricelli Andrea A Grimaldi Vincenzo V Coscioni Enrico E Salvatore Marco M Napoli Claudio C

Genes 20211202 12

<h4>Objectives</h4>Dilated cardiomyopathy (DCM) is characterized by a specific transcriptome. Since the DCM molecular network is largely unknown, the aim was to identify specific disease-related molecular targets combining an original machine learning (ML) approach with protein-protein interaction network.<h4>Methods</h4>The transcriptomic profiles of human myocardial tissues were investigated integrating an original computational approach, based on the Custom Decision Tree algorithm, in a diffe ...[more]

PMID: 34946895

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Dataset Information

Machine Learning and Bioinformatics Framework Integration to Potential Familial DCM-Related Markers Discovery.

Objectives

Methods

Results

Conclusions

Publications

Machine Learning and Bioinformatics Framework Integration to Potential Familial DCM-Related Markers Discovery.

Similar Datasets

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