Robustness of TransMarker concerning the selection and completeness of the prior knowledge network.
<p>Performance comparison of TransMarker when utilizing different prior interaction networks (HumanNet, InBioMap, STRINGdb) and under progressive random edge removal (–5%, –10%, –15%, and –20%) from the original RegNetwork prior. Bars represent across multiple runs for ACC, AUROC, and AUPRC.&l...
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| _version_ | 1849927641115656192 |
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| author | Fatemeh Keikha (20917848) |
| author2 | Chuanyuan Wang (22676721) Zhixia Yang (3180567) Zhi-Ping Liu (149304) |
| author2_role | author author author |
| author_facet | Fatemeh Keikha (20917848) Chuanyuan Wang (22676721) Zhixia Yang (3180567) Zhi-Ping Liu (149304) |
| author_role | author |
| dc.creator.none.fl_str_mv | Fatemeh Keikha (20917848) Chuanyuan Wang (22676721) Zhixia Yang (3180567) Zhi-Ping Liu (149304) |
| dc.date.none.fl_str_mv | 2025-11-24T18:36:44Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcbi.1013743.s015 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Robustness_of_TransMarker_concerning_the_selection_and_completeness_of_the_prior_knowledge_network_/30697966 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Genetics Biotechnology Cancer Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified wasserstein optimal transport real world dataset quantified via gromov enhancing diagnostic precision dynamic network index deep neural network ablation studies confirm regulatory role transitions gene regulatory networks combining regulatory rewiring analyzing disease evolution temporal expression dynamics reflect dynamic changes neglecting structural rewiring multilayer network models modeling disease progression state alignment provides understanding cancer progression state graph alignment disease state classification state specific biomarkers disease progression state alignment specific expression multilayer graph cancer progression disease state regulatory variability regulatory roles structural shifts significant changes prioritized biomarkers classification accuracy state single xlink "> topological features synthetic simulated ranked using powerful strategy overall performance meaningful shifts gats ), gastric adenocarcinoma gac ), framework designed findings suggest dni ), distinct layer contextualized embeddings cell data |
| dc.title.none.fl_str_mv | Robustness of TransMarker concerning the selection and completeness of the prior knowledge network. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Performance comparison of TransMarker when utilizing different prior interaction networks (HumanNet, InBioMap, STRINGdb) and under progressive random edge removal (–5%, –10%, –15%, and –20%) from the original RegNetwork prior. Bars represent across multiple runs for ACC, AUROC, and AUPRC.</p> <p>(EPS)</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_af1167a5d3d5356a63d98988838d36bd |
| identifier_str_mv | 10.1371/journal.pcbi.1013743.s015 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30697966 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Robustness of TransMarker concerning the selection and completeness of the prior knowledge network.Fatemeh Keikha (20917848)Chuanyuan Wang (22676721)Zhixia Yang (3180567)Zhi-Ping Liu (149304)MedicineGeneticsBiotechnologyCancerInfectious DiseasesBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedwasserstein optimal transportreal world datasetquantified via gromovenhancing diagnostic precisiondynamic network indexdeep neural networkablation studies confirmregulatory role transitionsgene regulatory networkscombining regulatory rewiringanalyzing disease evolutiontemporal expression dynamicsreflect dynamic changesneglecting structural rewiringmultilayer network modelsmodeling disease progressionstate alignment providesunderstanding cancer progressionstate graph alignmentdisease state classificationstate specific biomarkersdisease progressionstate alignmentspecific expressionmultilayer graphcancer progressiondisease stateregulatory variabilityregulatory rolesstructural shiftssignificant changesprioritized biomarkersclassification accuracystate singlexlink ">topological featuressynthetic simulatedranked usingpowerful strategyoverall performancemeaningful shiftsgats ),gastric adenocarcinomagac ),framework designedfindings suggestdni ),distinct layercontextualized embeddingscell data<p>Performance comparison of TransMarker when utilizing different prior interaction networks (HumanNet, InBioMap, STRINGdb) and under progressive random edge removal (–5%, –10%, –15%, and –20%) from the original RegNetwork prior. Bars represent across multiple runs for ACC, AUROC, and AUPRC.</p> <p>(EPS)</p>2025-11-24T18:36:44ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pcbi.1013743.s015https://figshare.com/articles/figure/Robustness_of_TransMarker_concerning_the_selection_and_completeness_of_the_prior_knowledge_network_/30697966CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306979662025-11-24T18:36:44Z |
| spellingShingle | Robustness of TransMarker concerning the selection and completeness of the prior knowledge network. Fatemeh Keikha (20917848) Medicine Genetics Biotechnology Cancer Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified wasserstein optimal transport real world dataset quantified via gromov enhancing diagnostic precision dynamic network index deep neural network ablation studies confirm regulatory role transitions gene regulatory networks combining regulatory rewiring analyzing disease evolution temporal expression dynamics reflect dynamic changes neglecting structural rewiring multilayer network models modeling disease progression state alignment provides understanding cancer progression state graph alignment disease state classification state specific biomarkers disease progression state alignment specific expression multilayer graph cancer progression disease state regulatory variability regulatory roles structural shifts significant changes prioritized biomarkers classification accuracy state single xlink "> topological features synthetic simulated ranked using powerful strategy overall performance meaningful shifts gats ), gastric adenocarcinoma gac ), framework designed findings suggest dni ), distinct layer contextualized embeddings cell data |
| status_str | publishedVersion |
| title | Robustness of TransMarker concerning the selection and completeness of the prior knowledge network. |
| title_full | Robustness of TransMarker concerning the selection and completeness of the prior knowledge network. |
| title_fullStr | Robustness of TransMarker concerning the selection and completeness of the prior knowledge network. |
| title_full_unstemmed | Robustness of TransMarker concerning the selection and completeness of the prior knowledge network. |
| title_short | Robustness of TransMarker concerning the selection and completeness of the prior knowledge network. |
| title_sort | Robustness of TransMarker concerning the selection and completeness of the prior knowledge network. |
| topic | Medicine Genetics Biotechnology Cancer Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified wasserstein optimal transport real world dataset quantified via gromov enhancing diagnostic precision dynamic network index deep neural network ablation studies confirm regulatory role transitions gene regulatory networks combining regulatory rewiring analyzing disease evolution temporal expression dynamics reflect dynamic changes neglecting structural rewiring multilayer network models modeling disease progression state alignment provides understanding cancer progression state graph alignment disease state classification state specific biomarkers disease progression state alignment specific expression multilayer graph cancer progression disease state regulatory variability regulatory roles structural shifts significant changes prioritized biomarkers classification accuracy state single xlink "> topological features synthetic simulated ranked using powerful strategy overall performance meaningful shifts gats ), gastric adenocarcinoma gac ), framework designed findings suggest dni ), distinct layer contextualized embeddings cell data |