Identification of Methylation Signatures and Rules for Sarcoma Subtypes by Machine Learning Methods.
Sarcoma, the second common type of solid tumor in children and adolescents, has a wide variety of subtypes that are often not properly diagnosed at an early stage, leading to late metastases and causing serious loss of life and property to patients and families. It exhibits a high degree of heteroge...
| Publicado en: | BioMed Research International pp. 1 - 12 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
12/28/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=161035707&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161035707 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 12/28/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 161035707 161035707 161035707 10.1155/2022/5297235 161035707 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Identification of Methylation Signatures and Rules for Sarcoma Subtypes by Machine Learning Methods. aug: au: Ren, Jingxin Zhou, XianChao Guo, Wei Feng, KaiYan Huang, Tao Cai, Yu-Dong affil: School of Life Sciences, Shanghai University, Shanghai 200444, China sug: subj: Machine Learning Methods Sarcoma Classification DNA Methylation Early Diagnosis Sarcoma Diagnosis Biological Markers Algorithms Decision Trees Random Forest Models, Theoretical Gene Expression Epigenomics Human ab: Sarcoma, the second common type of solid tumor in children and adolescents, has a wide variety of subtypes that are often not properly diagnosed at an early stage, leading to late metastases and causing serious loss of life and property to patients and families. It exhibits a high degree of heterogeneity at the cellular, molecular, and epigenetic levels, where DNA methylation has been proposed to play a role in the diagnosis of sarcoma subtypes. Thus, this study is aimed at finding potential biomarkers at the DNA methylation level to distinguish different sarcoma subtypes. A machine learning process was designed to analyse sarcoma samples, each of which was represented by lots of methylation sites. Irrelevant sites were removed using the Boruta method, and remaining sites related to the target variables were kept for further analyses. Afterward, three feature ranking methods (LASSO, LightGBM, and MCFS) were adopted to rank these features, and six classification models were constructed by combining incremental feature selection and two classification algorithms (decision tree and random forest). Among these models, the performance of RF model was higher than that of DT model under all three ranking conditions. The specific expression of genes obtained from the annotation of highly correlated methylation site features, such as PRKAR1B, INPP5A, and GLI3, was proven to be associated with sarcoma by publications. Moreover, the quantitative rules obtained by decision tree algorithm helped us to understand the essential differences between various sarcoma types and classify sarcoma subtypes, providing a new means of clinical identification and determining new therapeutic targets. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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