CASynergy algorithm workflow.
<p><b>(a)</b> Cell line-specific gene network extraction module: Using the expression data of key genes in the cell line and the PPI network , a disease-specific topological network is constructed; <b>(b)</b> Drug and cell line feature extraction and fusion module: The...
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2025
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| _version_ | 1852015764089864192 |
|---|---|
| author | Haitao Li (305080) |
| author2 | Long Zheng (694761) Lei Li (29537) Yiwei Chen (383344) Junjie Li (12724) Chunhou Zheng (15936521) Yansen Su (459998) |
| author2_role | author author author author author author |
| author_facet | Haitao Li (305080) Long Zheng (694761) Lei Li (29537) Yiwei Chen (383344) Junjie Li (12724) Chunhou Zheng (15936521) Yansen Su (459998) |
| author_role | author |
| dc.creator.none.fl_str_mv | Haitao Li (305080) Long Zheng (694761) Lei Li (29537) Yiwei Chen (383344) Junjie Li (12724) Chunhou Zheng (15936521) Yansen Su (459998) |
| dc.date.none.fl_str_mv | 2025-10-15T17:42:40Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcbi.1013567.g001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/CASynergy_algorithm_workflow_/30368514 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biochemistry Medicine Genetics Pharmacology Biotechnology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified unique molecular context improve treatment efficacy specific molecular interactions complex biological interactions attention synergy ), two benchmark datasets provides interpretable insights personalized cancer treatment causal attention model causal attention mechanism improvements allow casynergy cancer cell line causal attention cell line gene interactions attention module multimodal datasets interpretable prediction yet suffer tumor heterogeneity reliable way propose casynergy promising strategy prior approaches overcome resistance inadequate modeling challenging due casynergy offers casynergy introduces art models addresses limitations |
| dc.title.none.fl_str_mv | CASynergy algorithm workflow. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p><b>(a)</b> Cell line-specific gene network extraction module: Using the expression data of key genes in the cell line and the PPI network , a disease-specific topological network is constructed; <b>(b)</b> Drug and cell line feature extraction and fusion module: The fusion of drug combination features and disease cell line features forms a semantic network; <b>(c)</b> Drug combination synergy prediction module based on causal attention learning: By combining the topological and semantic networks, and utilizing a causal inference-based attention mechanism, the synergy of drug combinations is predicted.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_6a18d7cd17a06920229e8cf1663ae5f1 |
| identifier_str_mv | 10.1371/journal.pcbi.1013567.g001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30368514 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | CASynergy algorithm workflow.Haitao Li (305080)Long Zheng (694761)Lei Li (29537)Yiwei Chen (383344)Junjie Li (12724)Chunhou Zheng (15936521)Yansen Su (459998)BiochemistryMedicineGeneticsPharmacologyBiotechnologyCancerBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedunique molecular contextimprove treatment efficacyspecific molecular interactionscomplex biological interactionsattention synergy ),two benchmark datasetsprovides interpretable insightspersonalized cancer treatmentcausal attention modelcausal attention mechanismimprovements allow casynergycancer cell linecausal attentioncell linegene interactionsattention modulemultimodal datasetsinterpretable predictionyet suffertumor heterogeneityreliable waypropose casynergypromising strategyprior approachesovercome resistanceinadequate modelingchallenging duecasynergy offerscasynergy introducesart modelsaddresses limitations<p><b>(a)</b> Cell line-specific gene network extraction module: Using the expression data of key genes in the cell line and the PPI network , a disease-specific topological network is constructed; <b>(b)</b> Drug and cell line feature extraction and fusion module: The fusion of drug combination features and disease cell line features forms a semantic network; <b>(c)</b> Drug combination synergy prediction module based on causal attention learning: By combining the topological and semantic networks, and utilizing a causal inference-based attention mechanism, the synergy of drug combinations is predicted.</p>2025-10-15T17:42:40ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pcbi.1013567.g001https://figshare.com/articles/figure/CASynergy_algorithm_workflow_/30368514CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/303685142025-10-15T17:42:40Z |
| spellingShingle | CASynergy algorithm workflow. Haitao Li (305080) Biochemistry Medicine Genetics Pharmacology Biotechnology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified unique molecular context improve treatment efficacy specific molecular interactions complex biological interactions attention synergy ), two benchmark datasets provides interpretable insights personalized cancer treatment causal attention model causal attention mechanism improvements allow casynergy cancer cell line causal attention cell line gene interactions attention module multimodal datasets interpretable prediction yet suffer tumor heterogeneity reliable way propose casynergy promising strategy prior approaches overcome resistance inadequate modeling challenging due casynergy offers casynergy introduces art models addresses limitations |
| status_str | publishedVersion |
| title | CASynergy algorithm workflow. |
| title_full | CASynergy algorithm workflow. |
| title_fullStr | CASynergy algorithm workflow. |
| title_full_unstemmed | CASynergy algorithm workflow. |
| title_short | CASynergy algorithm workflow. |
| title_sort | CASynergy algorithm workflow. |
| topic | Biochemistry Medicine Genetics Pharmacology Biotechnology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified unique molecular context improve treatment efficacy specific molecular interactions complex biological interactions attention synergy ), two benchmark datasets provides interpretable insights personalized cancer treatment causal attention model causal attention mechanism improvements allow casynergy cancer cell line causal attention cell line gene interactions attention module multimodal datasets interpretable prediction yet suffer tumor heterogeneity reliable way propose casynergy promising strategy prior approaches overcome resistance inadequate modeling challenging due casynergy offers casynergy introduces art models addresses limitations |