Construction and evaluation of RF, SVM, GLM, and XGB machine models.
<p>(A) Boxplots showed the residuals of each machine learning model. Red dot represented the root mean square of residuals (RMSE). (B) ROC analysis of four machine learning models based on 5-fold cross-validation in the testing cohort.</p>
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
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| _version_ | 1852019695853502464 |
|---|---|
| author | Hongmin Luo (2142307) |
| author2 | Yuxuan Cao (12490494) Liping Guo (596697) Hui Li (32376) Yingying Yuan (9674249) Fan Lu (490738) |
| author2_role | author author author author author |
| author_facet | Hongmin Luo (2142307) Yuxuan Cao (12490494) Liping Guo (596697) Hui Li (32376) Yingying Yuan (9674249) Fan Lu (490738) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hongmin Luo (2142307) Yuxuan Cao (12490494) Liping Guo (596697) Hui Li (32376) Yingying Yuan (9674249) Fan Lu (490738) |
| dc.date.none.fl_str_mv | 2025-06-03T20:13:10Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0321636.g006 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Construction_and_evaluation_of_RF_SVM_GLM_and_XGB_machine_models_/29230241 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Microbiology Cell Biology Genetics Molecular Biology Evolutionary Biology Immunology Developmental Biology Marine Biology Cancer Infectious Diseases Biological Sciences not elsewhere classified ward &# 8217 responsive protein 12 potential biological markers logistic regression analysis gene expression omnibus via different algorithms related factor 2 differentially expressed genes distinguish different phenotypes determine different phenotypes immune cell subtypes consensus clustering based immune microenvironment characteristics nomogram constructed based key genes related describe disease heterogeneity related genes key genes consensus clustering immune microenvironment related pathways jointly constructed identified via disease clustering disease phenotypes immune score xylulose reductase xlink "> study focused study aimed positively correlated new clue negatively correlated learning algorithm high enrichment high accuracy disease risk diabetic nephropathy control samples cluster c2 cluster c1 clinical traits |
| dc.title.none.fl_str_mv | Construction and evaluation of RF, SVM, GLM, and XGB machine models. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>(A) Boxplots showed the residuals of each machine learning model. Red dot represented the root mean square of residuals (RMSE). (B) ROC analysis of four machine learning models based on 5-fold cross-validation in the testing cohort.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_ccb86c64f70ea3501edd4d0ce10fd50a |
| identifier_str_mv | 10.1371/journal.pone.0321636.g006 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29230241 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Construction and evaluation of RF, SVM, GLM, and XGB machine models.Hongmin Luo (2142307)Yuxuan Cao (12490494)Liping Guo (596697)Hui Li (32376)Yingying Yuan (9674249)Fan Lu (490738)MicrobiologyCell BiologyGeneticsMolecular BiologyEvolutionary BiologyImmunologyDevelopmental BiologyMarine BiologyCancerInfectious DiseasesBiological Sciences not elsewhere classifiedward &# 8217responsive protein 12potential biological markerslogistic regression analysisgene expression omnibusvia different algorithmsrelated factor 2differentially expressed genesdistinguish different phenotypesdetermine different phenotypesimmune cell subtypesconsensus clustering basedimmune microenvironment characteristicsnomogram constructed basedkey genes relateddescribe disease heterogeneityrelated geneskey genesconsensus clusteringimmune microenvironmentrelated pathwaysjointly constructedidentified viadisease clusteringdisease phenotypesimmune scorexylulose reductasexlink ">study focusedstudy aimedpositively correlatednew cluenegatively correlatedlearning algorithmhigh enrichmenthigh accuracydisease riskdiabetic nephropathycontrol samplescluster c2cluster c1clinical traits<p>(A) Boxplots showed the residuals of each machine learning model. Red dot represented the root mean square of residuals (RMSE). (B) ROC analysis of four machine learning models based on 5-fold cross-validation in the testing cohort.</p>2025-06-03T20:13:10ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0321636.g006https://figshare.com/articles/figure/Construction_and_evaluation_of_RF_SVM_GLM_and_XGB_machine_models_/29230241CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/292302412025-06-03T20:13:10Z |
| spellingShingle | Construction and evaluation of RF, SVM, GLM, and XGB machine models. Hongmin Luo (2142307) Microbiology Cell Biology Genetics Molecular Biology Evolutionary Biology Immunology Developmental Biology Marine Biology Cancer Infectious Diseases Biological Sciences not elsewhere classified ward &# 8217 responsive protein 12 potential biological markers logistic regression analysis gene expression omnibus via different algorithms related factor 2 differentially expressed genes distinguish different phenotypes determine different phenotypes immune cell subtypes consensus clustering based immune microenvironment characteristics nomogram constructed based key genes related describe disease heterogeneity related genes key genes consensus clustering immune microenvironment related pathways jointly constructed identified via disease clustering disease phenotypes immune score xylulose reductase xlink "> study focused study aimed positively correlated new clue negatively correlated learning algorithm high enrichment high accuracy disease risk diabetic nephropathy control samples cluster c2 cluster c1 clinical traits |
| status_str | publishedVersion |
| title | Construction and evaluation of RF, SVM, GLM, and XGB machine models. |
| title_full | Construction and evaluation of RF, SVM, GLM, and XGB machine models. |
| title_fullStr | Construction and evaluation of RF, SVM, GLM, and XGB machine models. |
| title_full_unstemmed | Construction and evaluation of RF, SVM, GLM, and XGB machine models. |
| title_short | Construction and evaluation of RF, SVM, GLM, and XGB machine models. |
| title_sort | Construction and evaluation of RF, SVM, GLM, and XGB machine models. |
| topic | Microbiology Cell Biology Genetics Molecular Biology Evolutionary Biology Immunology Developmental Biology Marine Biology Cancer Infectious Diseases Biological Sciences not elsewhere classified ward &# 8217 responsive protein 12 potential biological markers logistic regression analysis gene expression omnibus via different algorithms related factor 2 differentially expressed genes distinguish different phenotypes determine different phenotypes immune cell subtypes consensus clustering based immune microenvironment characteristics nomogram constructed based key genes related describe disease heterogeneity related genes key genes consensus clustering immune microenvironment related pathways jointly constructed identified via disease clustering disease phenotypes immune score xylulose reductase xlink "> study focused study aimed positively correlated new clue negatively correlated learning algorithm high enrichment high accuracy disease risk diabetic nephropathy control samples cluster c2 cluster c1 clinical traits |