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We began analyzing https://bmccardiovascdisord.biomedcentral.com/articles/10.1186/s12872-021-02147-7, but it redirected us to https://bmccardiovascdisord.biomedcentral.com/articles/10.1186/s12872-021-02147-7. The analysis below is for the second page.

Title[redir]:
Integration of transcriptomic data identifies key hallmark genes in hypertrophic cardiomyopathy | BMC Cardiovascular Disorders | Full Text
Description:
Background Hypertrophic cardiomyopathy (HCM) represents one of the most common inherited heart diseases. To identify key molecules involved in the development of HCM, gene expression patterns of the heart tissue samples in HCM patients from multiple microarray and RNA-seq platforms were investigated. Methods The significant genes were obtained through the intersection of two gene sets, corresponding to the identified differentially expressed genes (DEGs) within the microarray data and within the RNA-Seq data. Those genes were further ranked using minimum-Redundancy Maximum-Relevance feature selection algorithm. Moreover, the genes were assessed by three different machine learning methods for classification, including support vector machines, random forest and k-Nearest Neighbor. Results Outstanding results were achieved by taking exclusively the top eight genes of the ranking into consideration. Since the eight genes were identified as candidate HCM hallmark genes, the interactions between them and known HCM disease genes were explored through the protein–protein interaction (PPI) network. Most candidate HCM hallmark genes were found to have direct or indirect interactions with known HCM diseases genes in the PPI network, particularly the hub genes JAK2 and GADD45A. Conclusions This study highlights the transcriptomic data integration, in combination with machine learning methods, in providing insight into the key hallmark genes in the genetic etiology of HCM.

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Keywords {🔍}

genes, hcm, pubmed, article, google, scholar, data, expression, cas, central, gene, microarray, rnaseq, jak, hallmark, degs, results, candidate, analysis, classification, cardiomyopathy, samples, datasets, information, bmc, hypertrophic, disease, study, cardiac, signaling, fig, dataset, including, obtained, network, svm, knn, pathway, identified, interaction, gadda, showed, bioinformatics, table, role, wang, heart, full, jakstat, res,

Topics {✒️}

ang ii/tgf-1beta/smad3 pathway springer nature jak/stat/socs signaling circuits classification algorithms k-nearest-neighbor rna-seq-based transcriptome analysis 5’rna-seq identifies fhl1 calcium-mediated signaling pathway myh7/mybpc3 genotype information strand-specific rna-seq myh7/mybpc3 genotype affects high-fat diet rich inter-ventricular septum hypertrophy k-nearest neighbor models background detection levels rna-seq data analysis calcium-handling genes minimum-redundancy maximum-relevance myh7/mybpc3 genotype positive pressure overload hypertrophy rna-seq data separately figures 5 discussion availability sharing nonfamilial hypertrophic cardiomyopathy ten-fold cross-validation microarray-based cancer classification high-throughput sequencing data sequence alignment/map format janus kinase/signal transducer rna-seq data sets high-confidence experimental interactions human hypertrophic cardiomyopathy author information authors bmc cardiovasc disord privacy choices/manage cookies instance-based learning method authors scientific editing additional information publisher' hypertrophic stress signaling key hallmark genes jak-stat signaling machine learning methods myocardial hypertrophic remodeling candidate hallmark gene genetic background jak/stat pathway jak-stat pathway k-nearest neighbor cell stem cell

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  • Are random forests better than support vector machines for microarray-based cancer classification?
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Schema {🗺️}

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