Overview of the TransMarker framework for identifying DNBs from single cell time course data.
<p>(A) State specific gene expression profiles are integrated with a prior regulatory network to construct attributed gene interaction graphs. (B) A set of rewired graphs is generated to represent disease progression across multiple states. (C) Topological Feature Integration incorporates both...
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2025
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| _version_ | 1849927641110413312 |
|---|---|
| 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 | 2025-11-24T18:36:47Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcbi.1013743.g001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Overview_of_the_TransMarker_framework_for_identifying_DNBs_from_single_cell_time_course_data_/30697975 |
| 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 | Overview of the TransMarker framework for identifying DNBs from single cell time course data. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>(A) State specific gene expression profiles are integrated with a prior regulatory network to construct attributed gene interaction graphs. (B) A set of rewired graphs is generated to represent disease progression across multiple states. (C) Topological Feature Integration incorporates both local and global structural properties into each graph. (D) GATs are applied to learn contextualized embeddings for each disease state. (E) Gromov-Wasserstein optimal transport computes alignment scores between state specific networks to identify genes with high structural shifts. (F) A union subnetwork of selected genes is used to compute a DNI, identifying DNBs based on network variability. (G) The resulting DNBs and initial feature matrices are input to a DNN for multiclass classification across disease states.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_7d85da3b753d7b56c86442c1ca556453 |
| identifier_str_mv | 10.1371/journal.pcbi.1013743.g001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30697975 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Overview of the TransMarker framework for identifying DNBs from single cell time course data.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>(A) State specific gene expression profiles are integrated with a prior regulatory network to construct attributed gene interaction graphs. (B) A set of rewired graphs is generated to represent disease progression across multiple states. (C) Topological Feature Integration incorporates both local and global structural properties into each graph. (D) GATs are applied to learn contextualized embeddings for each disease state. (E) Gromov-Wasserstein optimal transport computes alignment scores between state specific networks to identify genes with high structural shifts. (F) A union subnetwork of selected genes is used to compute a DNI, identifying DNBs based on network variability. (G) The resulting DNBs and initial feature matrices are input to a DNN for multiclass classification across disease states.</p>2025-11-24T18:36:47ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pcbi.1013743.g001https://figshare.com/articles/figure/Overview_of_the_TransMarker_framework_for_identifying_DNBs_from_single_cell_time_course_data_/30697975CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306979752025-11-24T18:36:47Z |
| spellingShingle | Overview of the TransMarker framework for identifying DNBs from single cell time course data. 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 | Overview of the TransMarker framework for identifying DNBs from single cell time course data. |
| title_full | Overview of the TransMarker framework for identifying DNBs from single cell time course data. |
| title_fullStr | Overview of the TransMarker framework for identifying DNBs from single cell time course data. |
| title_full_unstemmed | Overview of the TransMarker framework for identifying DNBs from single cell time course data. |
| title_short | Overview of the TransMarker framework for identifying DNBs from single cell time course data. |
| title_sort | Overview of the TransMarker framework for identifying DNBs from single cell time course data. |
| 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 |