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10.1186/s12915-025-02302-y http://scihub22266oqcxt.onion/10.1186/s12915-025-02302-y 40611149!12226924!40611149     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\40611149.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       BMC+Biol 2025 ; 23 (1): 200 Nephropedia Template TP {{tp|p=40611149|t=2025. Accurate prediction of synergistic drug combination using a multi-source information fusion framework |pdf=|usr=}}{{40611149}} gab.com Text Accurate prediction of synergistic drug combination using a multi-source information fusion framework JO: BMC Biol http://www.kidney.de/pget.php?&tw=1&pmid=40611149 #FOAvip BACKGROUND: Accurately predicting synergistic drug combinations is critical for complex disease therapy. However, the vast search space of potential drug combinations poses significant challenges for identification through biological experiments alone. Nowadays, deep learning is widely applied in this field. However, most methods overlook the important role of protein-protein interaction networks formed by gene expression products and the pharmacophore information of drugs in predicting drug synergy. RESULTS: We propose MultiSyn, a multi-source information integration method for the accurate prediction of synergistic drug combinations. Specifically, we design a semi-supervised learning framework using an attributed graph neural network to integrate protein-protein interaction networks of gene expression products with multi-omics data, constructing initial cell line representations that incorporate multi-source information. Furthermore, we refine the initial cell line representation by adaptively integrating it with normalized gene expression profiles, enabling the extraction of cell line features that encapsulate global information. In addition, we decompose drugs into fragments containing pharmacophore information based on chemical reaction rules and construct a heterogeneous graph comprising atomic and fragment nodes. To enhance the capture of molecular structural information, we introduce a heterogeneous graph transformer to learn multi-view representations of heterogeneous molecular graphs. Extensive experiments show that MultiSyn outperforms several classical and state-of-the-art baselines in synergistic drug combination prediction tasks. CONCLUSIONS: This study provides a powerful tool for inferring promising synergistic drug combinations. By leveraging attention mechanisms and pharmacophore information, MultiSyn identifies key substructures that are critical for synergy. Further visualization and case studies validate its effectiveness in capturing biologically meaningful features and identifying potential drug combinations. Twit Text FOAVip Accurate prediction of synergistic drug combination using a multi-source information fusion framework ????BMC Biol http://www.kidney.de/pget.php?&tw=1&pmid=40611149 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=40611149 #FOAvip English Wikipedia <ref>{{Cite pmid|40611149}}</ref> Accurate prediction of synergistic drug combination using a multi-source information fusion framework #MMPMID40611149 Jin S; Long H; Huang A; Wang J; Yu X; Xu Z; Xu J BMC Biol 2025[Jul]; 23 (1): 200 PMID40611149show ga BACKGROUND: Accurately predicting synergistic drug combinations is critical for complex disease therapy. However, the vast search space of potential drug combinations poses significant challenges for identification through biological experiments alone. Nowadays, deep learning is widely applied in this field. However, most methods overlook the important role of protein-protein interaction networks formed by gene expression products and the pharmacophore information of drugs in predicting drug synergy. RESULTS: We propose MultiSyn, a multi-source information integration method for the accurate prediction of synergistic drug combinations. Specifically, we design a semi-supervised learning framework using an attributed graph neural network to integrate protein-protein interaction networks of gene expression products with multi-omics data, constructing initial cell line representations that incorporate multi-source information. Furthermore, we refine the initial cell line representation by adaptively integrating it with normalized gene expression profiles, enabling the extraction of cell line features that encapsulate global information. In addition, we decompose drugs into fragments containing pharmacophore information based on chemical reaction rules and construct a heterogeneous graph comprising atomic and fragment nodes. To enhance the capture of molecular structural information, we introduce a heterogeneous graph transformer to learn multi-view representations of heterogeneous molecular graphs. Extensive experiments show that MultiSyn outperforms several classical and state-of-the-art baselines in synergistic drug combination prediction tasks. CONCLUSIONS: This study provides a powerful tool for inferring promising synergistic drug combinations. By leveraging attention mechanisms and pharmacophore information, MultiSyn identifies key substructures that are critical for synergy. Further visualization and case studies validate its effectiveness in capturing biologically meaningful features and identifying potential drug combinations. |*Drug Synergism[MESH] |Drug Combinations[MESH] |Humans[MESH] |Neural Networks, Computer[MESH]Accurate prediction of synergistic drug combination using a multi-sour(epmc).pdf DeepDyve Pubget Overpricing