Akond Z., Alam M., Mollah Md.N.H. 2018. Biomarker identification from RNA-seq data using a robust statistical approach. Bioinformation. 14 (4), 153–163.

Article  PubMed  PubMed Central  Google Scholar 

Tang M., Sun J., Shimizu K., Kadota K. 2015. Evaluation of methods for differential expression analysis on multi-group RNA-seq count data. BMC Bioinf. 16 (1), 360.

Article  Google Scholar 

Barbiero P., Squillero G., Tonda A. 2020. Modeling generalization in machine learning: A methodological and computational study. arXiv. 2006.15680.

Robinson M.D., McCarthy D.J., Smyth G.K. 2010. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 26 (1), 139–140.

Article  CAS  PubMed  Google Scholar 

Smyth G.K. 2005. Limma: Linear models for microarray data. In Bioinformatics and Computational Biology Solutions using R and Bioconductor. New York: Springer.

Google Scholar 

Benjamini Y., Hochberg Y. 1997. Multiple hypotheses testing with weights. Scand. J. Stat. 24 (3), 407–418.

Article  Google Scholar 

Holm S. 1979. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6 (2), 65–70.

Google Scholar 

Gui J., Tosteson T.D., Borsuk M. 2012. Weighted multiple testing procedures for genomic studies. BioData Mining. 5 (1), 4.

Article  PubMed  PubMed Central  Google Scholar 

Basu P., Cai T. T., Das K., Sun W 2018. Weighted false discovery rate control in large-scale multiple testing. J. Am. Stat. Assoc. 113 (523), 1172–1183.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Mann H.B., Whitney D.R. 1947. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18 (1), 50–60.

Article  Google Scholar 

Benjamini Y., Hochberg Y. 1995. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc., Ser. B (Methodol.). 57 (1), 289–300.

Google Scholar 

Genovese C.R., Roeder K., Wasserman L. 2006. False discovery control with p-value weighting. Biometrika. 93 (3), 509–524.

Article  Google Scholar 

Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D., Brucher M., Duchesnay E. 2011. Scikit-learn: Machine learning in python. J. Machine Learning Res. 12, 2825–2830.

Google Scholar 

Anfinson M., Fitts R.H., Lough J.W., James J.M., Simpson P.M., Handler S.S., Mitchell M.E., Tomita-Mitchell A. 2022. Significance of α-myosin heavy chain (MYH6) variants in hypoplastic left heart syndrome and related cardiovascular diseases. J. Cardiovasc. Dev. Dis. 9 (5), 144.

CAS  PubMed  PubMed Central  Google Scholar 

Ntelios D., Meditskou S., Efthimiadis G., Pitsis A., Zegkos T., Parcharidou D., Theotokis P., Alexouda S., Karvounis H., Tzimagiorgis G. 2022. α-Myosin heavy chain (MYH6) in hypertrophic cardiomyopathy: Prominent expression in areas with vacuolar degeneration of myocardial cells. Pathol. Int. 72 (5), 308–310.

Article  CAS  PubMed  Google Scholar 

Suzuki T., Saito K., Yoshikawa T., Hirono K., Hata Y., Nishida N., Yasuda K., Nagashima M. 2022. A double heterozygous variant in MYH6 and MYH7 associated with hypertrophic cardiomyopathy in a Japanese family. J. Cardiol. Cases. 25 (4), 213–217.

Article  PubMed  Google Scholar 

Michalski M., Świerzko A.S., Pągowska-Klimek I., Niemir Z.I., Mazerant K., Domżalska-Popadiuk I., Moll M., Cedzyński M. 2015. Primary ficolin-3 deficiency—is it associated with increased susceptibility to infections? Immunobiology. 220 (6), 711–713.

Article  CAS  PubMed  Google Scholar 

Prohászka Z., Munthe-Fog L., Ueland T., Gombos T., Yndestad A., Förhécz Z., Skjoedt MO, Pozsonyi Z., Gustavsen A., Jánoskuti L., Karádi I., Gullestad L., Dahl C.P., Askevold E.T., Füst G., Aukrust P., Mollnes T.E., Garred P. 2013. Association of ficolin-3 with severity and outcome of chronic heart failure. PLoS One. 8 (4), e60976.

Article  PubMed  PubMed Central  Google Scholar 

Li D., Lin H., Li L. 2020. Multiple feature selection strategies identified novel cardiac gene expression signature for heart failure. Front. Physiol. 11, 604241.

Article  PubMed  PubMed Central  Google Scholar 

Song H., Chen S., Zhang T., Huang X., Zhang Q., Li C., Chen C., Chen S., Liu D., Wang J., Tu Y., Wu Y., Liu Y. 2022. Integrated strategies of diverse feature selection methods identify aging-based reliable gene signatures for ischemic cardiomyopathy. Front. Mol. Biosci. 9, 805235.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wie J., Kim B.J., Myeong J., Ha K., Jeong S.J., Yang D., Kim E., Jeon J.H., So I. 2015. The roles of Rasd1 small G proteins and leptin in the activation of TRPC4 transient receptor potential channels. Channels. 9 (4), 186–195.

Article  PubMed  PubMed Central  Google Scholar 

Kemppainen R.J., Behrend E.N. 1998. Dexamethasone rapidly induces a novel Ras superfamily member-related gene in AtT-20 cells. J. Biol. Chem. 273 (6), 3129–3131.

Article  CAS  PubMed  Google Scholar 

McGrath M.F., Ogawa T., De Bold A.J. 2012. Ras dexamethasone-induced protein 1 is a modulator of hormone secretion in the volume overloaded heart. Am. J. Physiol. Heart Circ. Physiol. 302 (9), H1826–H1837.

Article  CAS  PubMed  Google Scholar 

Baker C., Belbin O., Kalsheker N., Morgan K. 2007. SERPINA3 (aka alpha-1-antichymotrypsin). Front. Biosci. 12 (8–12), 2821–2835.

Article  CAS  PubMed  Google Scholar 

de Mezer M., Rogaliński J., Przewoźny S., Chojni-cki M., Niepolski L., Sobieska M., Przystańska A. 2023. SERPINA3: Stimulator or inhibitor of pathological changes. Biomedicines. 11 (1), 156.

Article  CAS  PubMed  PubMed Central  Google Scholar 

You H., Dong M. 2023. Prediction of diagnostic gene biomarkers for hypertrophic cardiomyopathy by integrated machine learning. J. Int. Med. Res. 51 (11), 03000605231213781.

Article  CAS  PubMed  PubMed Central  Google Scholar