Titelius E, Cook E, Spas J et al (2018) Emotion dysregulation mediates the Relationship between Child Maltreatment and Non-suicidal Self-Injury. Journal of aggression, maltreatment & trauma 27: 323 – 31. https://doi.org/10.1080/10926771.2017.1338814

Ren K, Wang L, Wang Y et al (2022) Wound age estimation based on next-generation sequencing: fitting the optimal index system using machine learning. Forensic Sci Int Genet 59:102722. https://doi.org/10.1016/j.fsigen.2022.102722

Article  CAS  PubMed  Google Scholar 

Fang X, Miao R, Wei J, Wu H, Tian J (2022) Advances in multi-omics study of biomarkers of glycolipid metabolism disorder. Comput Struct Biotechnol J 20:5935–5951. https://doi.org/10.1016/j.csbj.2022.10.030

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hassan Gaballah M, Fukuta M, Maeno Y et al (2016) Simultaneous time course analysis of multiple markers based on DNA microarray in incised wound in skeletal muscle for wound aging. Forensic Sci Int 266:357–368. https://doi.org/10.1016/j.forsciint.2016.06.027

Article  CAS  PubMed  Google Scholar 

Xu J, Zhao R, Xue Y et al (2018) RNA-seq profiling reveals differentially expressed genes as potential markers for vital reaction in skin contusion: a pilot study. Forensic Sci Res 3:153–160. https://doi.org/10.1080/20961790.2017.1349639

Article  PubMed  Google Scholar 

Kumar K, Cowley M, Davis R (2019) Next-generation sequencing and Emerging technologies. Semin Thromb Hemost 45:661–673. https://doi.org/10.1055/s-0039-1688446

Article  CAS  PubMed  Google Scholar 

Hu T, Chitnis N, Monos D, Dinh A (2021) Next-generation sequencing technologies: an overview. Hum Immunol 82:801–811. https://doi.org/10.1016/j.humimm.2021.02.012

Article  CAS  PubMed  Google Scholar 

Sun J, Zhu X, Dong T et al (2017) An up, no change, or down system: time-dependent expression of mRNAs in contused skeletal muscle of rats used for wound age estimation. Forensic Sci Int 272:104–110. https://doi.org/10.1016/j.forsciint.2017.01.012

Article  CAS  PubMed  Google Scholar 

Goeman J, Bühlmann P (2007) Analyzing gene expression data in terms of gene sets: methodological issues. Bioinf (Oxford England) 23:980–987. https://doi.org/10.1093/bioinformatics/btm051

Article  CAS  Google Scholar 

Wang L, Sun L, Sun H et al (2023) GPR65 as a potential immune checkpoint regulates the immune microenvironment according to pan-cancer analysis. Heliyon 9:e13617. https://doi.org/10.1016/j.heliyon.2023.e13617

Article  CAS  PubMed  PubMed Central  Google Scholar 

Dang L, Li J, Bai X et al (2023) Novel Prediction Method Applied to Wound Age Estimation: Developing a Stacking Ensemble Model to Improve Predictive Performance Based on Multi-mRNA. Diagnostics (Basel, Switzerland) 13. https://doi.org/10.3390/diagnostics13030395

Konczal M, Koteja P, Stuglik M, Radwan J, Babik W (2014) Accuracy of allele frequency estimation using pooled RNA-Seq. Molecular ecology resources 14: 381 – 92. https://doi.org/10.1111/1755-0998.12186

Kallio MA, Tuimala JT, Hupponen T, Klemelä P, Korpelainen EI (2011) Chipster: user-friendly analysis software for microarray and other high-throughput data. BMC Genomics 12

Kim D, Langmead B, Salzberg S (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12:357–360. https://doi.org/10.1038/nmeth.3317

Article  CAS  PubMed  PubMed Central  Google Scholar 

Anders S, Pyl PT, Huber W (2014) HTSeq - A Python framework to work with high-throughput sequencing data. Bioinformatics

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

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chen Y, Lun A, Smyth G (2016) From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using rsubread and the edgeR quasi-likelihood pipeline. F1000Research 5:1438. https://doi.org/10.12688/f1000research.8987.2

Article  PubMed  PubMed Central  Google Scholar 

Nueda M, Tarazona S, Conesa A (2014) Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series. Bioinf (Oxford England) 30:2598–2602. https://doi.org/10.1093/bioinformatics/btu333

Article  CAS  Google Scholar 

Ernst J, Bar-Joseph Z (2006) STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics 7:191. https://doi.org/10.1186/1471-2105-7-191

Article  CAS  PubMed  PubMed Central  Google Scholar 

Szklarczyk D, Morris J, Cook H et al (2017) The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 45:D362–D8. https://doi.org/10.1093/nar/gkw937

Article  CAS  PubMed  Google Scholar 

Chin C, Chen S, Wu H, Ho C, Ko M, Lin C (2014) cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC systems biology: S11. https://doi.org/10.1186/1752-0509-8-s4-s11

Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30. https://doi.org/10.1093/nar/28.1.27

Article  CAS  PubMed  PubMed Central  Google Scholar 

Liao Y, Wang J, Jaehnig E, Shi Z, Zhang B (2019) WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res 47:W199–W205. https://doi.org/10.1093/nar/gkz401

Article  CAS  PubMed  PubMed Central  Google Scholar 

Janky R, Verfaillie A, Imrichová H et al (2014) iRegulon: from a gene list to a gene regulatory network using large motif and track collections. PLoS Comput Biol 10:e1003731. https://doi.org/10.1371/journal.pcbi.1003731

Article  CAS  PubMed  PubMed Central  Google Scholar 

Taminau J, Meganck S, Lazar C et al (2012) Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages. BMC Bioinformatics 13:335. https://doi.org/10.1186/1471-2105-13-335

Article  PubMed  PubMed Central  Google Scholar 

Ritchie ME, Phipson B, Wu D et al (2015) Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47–e. https://doi.org/10.1093/nar/gkv007

Article  CAS  PubMed  PubMed Central  Google Scholar 

Speiser J, Miller M, Tooze J, Ip E (2019) A comparison of Random Forest Variable selection methods for classification prediction modeling. Expert Syst Appl 134:93–101. https://doi.org/10.1016/j.eswa.2019.05.028

Article  PubMed  PubMed Central  Google Scholar 

Brown M, Grundy W, Lin D et al (2000) Proc Natl Acad Sci USA 97:262–267. https://doi.org/10.1073/pnas.97.1.262. Knowledge-based analysis of microarray gene expression data by using support vector machines

Savalia S, Emamian V (2018) Cardiac arrhythmia classification by Multi-layer Perceptron and Convolution neural networks. Bioeng (Basel Switzerland) 5. https://doi.org/10.3390/bioengineering5020035

Zhu X, Yin T, Zhang T et al (2022) Identification of immune-related genes in patients with acute myocardial infarction using machine learning methods. J Inflamm Res : 3305–3321

Uchitomi R, Hatazawa Y, Senoo N et al (2019) Metabolomic Analysis of Skeletal Muscle in aged mice. Sci Rep 9:10425. https://doi.org/10.1038/s41598-019-46929-8

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ali AM, Kunugi H (2021) Skeletal muscle damage in COVID-19: a call for action. Med (Kaunas) 57. https://doi.org/10.3390/medicina57040372

Relaix F, Bencze M, Borok M et al (2021) Perspectives on skeletal muscle stem cells. Nat Commun 12:692. https://doi.org/10.1038/s41467-020-20760-6

Article  CAS  PubMed  PubMed Central  Google Scholar 

Schmidt M, Schüler S, Hüttner S, von Eyss B, von Maltzahn J (2019) Adult stem cells at work: regenerating skeletal muscle. Cell Mol Life Sci 76:2559–2570. https://doi.org/10.1007/s00018-019-03093-6

Article  CAS  PubMed  PubMed Central  Google Scholar 

Anderson J (2022) Key concepts in muscle regeneration: muscle cellular ecology integrates a gestalt of cellular cross-talk, motility, and activity to remodel structure and restore function. Eur J Appl Physiol 122:273–300. https://doi.org/10.1007/s00421-021-04865-4

Article  PubMed  Google Scholar 

Tian Z, Jiang S, Zhang M et al (2016) Detection of satellite cells during skeletal muscle wound healing in rats: time-dependent expressions of Pax7 and MyoD in relation to wound age. Int J Legal Med 130:163–172. https://doi.org/10.1007/s00414-015-1251-x

Article  PubMed  Google Scholar 

Zhang L, Yang Y, Chai L et al (2022) A deep learning model to identify gene expression level using cobinding transcription factor signals. Briefings in bioinformatics 23. https://doi.org/10.1093/bib/bbab501

Luo M, Zhou S, Feng D et al (2016) Runt-related transcription factor 1 (RUNX1) binds to p50 in macrophages and enhances TLR4-triggered inflammation and septic shock. J Biol Chem 291:22011–22020. https://doi.org/10.1074/jbc.M116.715953

Article  CAS  PubMed  PubMed Central  Google Scholar 

Johnson B, Stevenson A, Prêle C, Fear M, Wood F (2020) The role of IL-6 in skin fibrosis and cutaneous Wound Healing. Biomedicines 8. https://doi.org/10.3390/biomedicines8050101

Xiao T, Yan Z, Xiao S, Xia Y (2020) Proinflammatory cytokines regulate epidermal stem cells in wound epithelialization. Stem Cell Res Ther 11:232. https://doi.org/10.1186/s13287-020-01755-y

Article  PubMed  PubMed Central  Google Scholar 

Syafruddin S, Mohtar M, Wan Mohamad Nazarie W, Low T (2020) Two Sides of the Same Coin: The Roles of KLF6 in Physiology and Pathophysiology. Biomolecules 10. https://doi.org/10.3390/biom10101378

Arabpour M, Saghazadeh A, Rezaei N (2021) Anti-inflammatory and M2 macrophage polarization-promoting effect of mesenchymal stem cell-derived exosomes. Int Immunopharmacol 97:107823. https://doi.org/10.1016/j.intimp.2021.107823

Article  CAS  PubMed  Google Scholar