Moghadam ARE, Moghadam MT, Hemadi M, et al. Oocyte quality and aging. JBRA Assist Reprod. 2022;26:105–22. https://doi.org/10.5935/1518-0557.20210026.

Article  PubMed  PubMed Central  Google Scholar 

Dunkley S, Mogessie B. Actin limits egg aneuploidies associated with female reproductive aging. Sci Adv. 2023;9:eadc9161. https://doi.org/10.1126/sciadv.adc9161.

Article  PubMed  PubMed Central  Google Scholar 

Ferreira AF, Soares M, Almeida-Santos T, et al. Aging and oocyte competence: a molecular cell perspective. WIREs Mech Dis. 2023;15:e1613. https://doi.org/10.1002/wsbm.1613.

Article  CAS  PubMed  Google Scholar 

Liang J, Huang F, Song Z, et al. Impact of NAD+ metabolism on ovarian aging. Immun Ageing. 2023;20:70. https://doi.org/10.1186/s12979-023-00398-w.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wang X, Wang L, Xiang W. Mechanisms of ovarian aging in women: a review. J Ovarian Res. 2023;16:67. https://doi.org/10.1186/s13048-023-01151-z.

Article  PubMed  PubMed Central  Google Scholar 

Ntostis P, Iles D, Kokkali G, et al. The impact of maternal age on gene expression during the GV to MII transition in euploid human oocytes. Hum Reprod. 2021;37:80–92. https://doi.org/10.1093/humrep/deab226.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Charles S, Natarajan J. Integrated regulatory network based on lncRNA-miRNA-mRNA-TF reveals key genes and sub-networks associated with dilated cardiomyopathy. Comput Biol Chem. 2021;92:107500. https://doi.org/10.1016/j.compbiolchem.2021.107500.

Article  CAS  PubMed  Google Scholar 

Tong W, Wenze G, Libing H, et al. Exploration of shared TF-miRNA-mRNA and mRNA-RBP-pseudogene networks in type 2 diabetes mellitus and breast cancer. Front Immunol. 2022;13:915017. https://doi.org/10.3389/fimmu.2022.915017.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Li Y, Tang M, Zhang FJ, et al. Screening of ulcerative colitis biomarkers and potential pathways based on weighted gene co-expression network, machine learning and ceRNA hypothesis. Hereditas. 2022;159:42. https://doi.org/10.1186/s41065-022-00259-4.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Tang Q, Su Q, Wei L, et al. Identifying potential biomarkers for non-obstructive azoospermia using WGCNA and machine learning algorithms. Front Endocrinol. 2023;14:1108616. https://doi.org/10.3389/fendo.2023.1108616.

Article  Google Scholar 

Liao Y, Liu Q, Xiao C, et al. Machine learning and experimental validation to construct a metastasis-related gene signature and ceRNA network for predicting osteosarcoma prognosis. J Orthop Surg Res. 2022;17:516. https://doi.org/10.1186/s13018-022-03386-w.

Article  PubMed  PubMed Central  Google Scholar 

Wan C, Li Y. Integrative analysis of mRNA-miRNA-TFs reveals the key regulatory connections involved in basal cell carcinoma. Arch Dermatol Res. 2020;312:133–43. https://doi.org/10.1007/s00403-019-02002-y.

Article  PubMed  Google Scholar 

Deng J, Fu F, Zhang F, et al. Construct ceRNA network and risk model of breast cancer using machine learning methods under the mechanism of cuproptosis. Diagnostics. 2023;13:1203. https://doi.org/10.3390/diagnostics13061203.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhang J-J, Liu X, Chen L, et al. Advanced maternal age alters expression of maternal effect genes that are essential for human oocyte quality. Aging. 2020;12:3950–61. https://doi.org/10.18632/aging.102864.

Article  PubMed  PubMed Central  Google Scholar 

Yuan L, Yin P, Yan H, et al. Single-cell transcriptome analysis of human oocyte ageing. J Cellular Molecular Medi. 2021;25:6289–303. https://doi.org/10.1111/jcmm.16594.

Article  CAS  Google Scholar 

Llonch S, Barragán M, Nieto P, et al. Single human oocyte transcriptome analysis reveals distinct maturation stage-dependent pathways impacted by age. Aging Cell. 2021;20:e13360. https://doi.org/10.1111/acel.13360.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Karpiński EA, Skrzypczak AR. The significance of angling in stress reduction during the COVID-19 pandemic—environmental and socio-economic implications. IJERPH. 2022;19:4346. https://doi.org/10.3390/ijerph19074346.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Smyth GK, et al. limma: linear models for microarray data. In: Gentleman R, Carey VJ, Huber W, et al., editors. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. New York: Springer-Verlag; 2005. p. 397–420.

Chapter  Google Scholar 

Xu J, Zhou H, Cheng Y, et al. Identifying potential signatures for atherosclerosis in the context of predictive, preventive, and personalized medicine using integrative bioinformatics approaches and machine-learning strategies. EPMA J. 2022;13:433–49. https://doi.org/10.1007/s13167-022-00289-y.

Article  PubMed  PubMed Central  Google Scholar 

Harrow J, Frankish A, Gonzalez JM, et al. GENCODE: the reference human genome annotation for The ENCODE project. Genome Res. 2012;22:1760–74. https://doi.org/10.1101/gr.135350.111.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hsu S-D, Lin F-M, Wu W-Y, et al. miRTarBase: a database curates experimentally validated microRNA–target interactions. Nucleic Acids Res. 2011;39:D163–9. https://doi.org/10.1093/nar/gkq1107.

Article  CAS  PubMed  Google Scholar 

Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504. https://doi.org/10.1101/gr.1239303.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yu G, Wang L-G, Han Y, et al. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: J Integrative Biol. 2012;16:284–7. https://doi.org/10.1089/omi.2011.0118.

Article  CAS  Google Scholar 

Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008;9:559. https://doi.org/10.1186/1471-2105-9-559.

Article  CAS  Google Scholar 

He R, Guan C, Zhao X, et al. Expression of immune related genes and possible regulatory mechanisms in different stages of non-alcoholic fatty liver disease. Front Immunol. 2024;15:1364442. https://doi.org/10.3389/fimmu.2024.1364442.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Huang M-L, Hung Y-H, Lee WM, et al. SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier. Scientific World J. 2014;2014:1–10. https://doi.org/10.1155/2014/795624.

Article  Google Scholar 

Svetnik V, Liaw A, Tong C, et al. Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci. 2003;43:1947–58. https://doi.org/10.1021/ci034160g.

Article  CAS  PubMed  Google Scholar 

Robin X, Turck N, Hainard A, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 2011;12:77. https://doi.org/10.1186/1471-2105-12-77.

Article  Google Scholar 

Warde-Farley D, Donaldson SL, Comes O, et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010;38:W214–20. https://doi.org/10.1093/nar/gkq537.

Article  CAS  PubMed