MLG2Net: Molecular Global Graph Network for Drug Response Prediction in Lung Cancer Cell Lines.
Drug response prediction (DRP) is a central task in the era of precision medicine. Over the past decade, the emergence of deep learning (DL) has greatly contributed to addressing DRP challenges. Notably, the prediction of DRP for cancer cell lines benefits significantly from data availability for mo...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 8 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
4/10/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184386320&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184386320 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 4/10/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184386320 184386320 184386320 10.1007/s10916-025-02182-3 184386320 ppf: 1 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: MLG2Net: Molecular Global Graph Network for Drug Response Prediction in Lung Cancer Cell Lines. aug: au: Tran, Thi-Oanh Nguyen, Thanh-Huy Nguyen, Tuan Tung Le, Nguyen Quoc Khanh affil: https://ror.org/05ecec111 Hematology and Blood Transfusion Center, Bach Mai Hospital, Hanoi, Viet Nam sug: subj: Lung Neoplasms Drug Therapy Antineoplastic Agents Pharmacodynamics Drug Screening Assays, Antitumor Methods Prediction Models Cell Line, Tumor Drug Effects Deep Learning Neural Networks (Computer) Pharmacogenetics Human Funding Source Lung Neoplasms Familial and Genetic Antineoplastic Agents Therapeutic Use Treatment Outcomes Adenocarcinoma of Lung Carcinoma, Squamous Cell Precision Individualized Medicine Pathology, Molecular ab: Drug response prediction (DRP) is a central task in the era of precision medicine. Over the past decade, the emergence of deep learning (DL) has greatly contributed to addressing DRP challenges. Notably, the prediction of DRP for cancer cell lines benefits significantly from data availability for model development. However, an effective predictive model is still challenging due to issues with data quality, high-dimensional data, and multi-omics data integration. In this study, we introduce MLG2Net, a deep-learning model inspired by graph neural networks designed to predict DRP in lung cancer cell lines based on pharmacogenomics data. Our model comprises two key components: drug SMILES described by local and global graph networks and cell line genomics are illustrated as a map. Our results show that MLG2Net outperforms three reference graph networks. MLG2Net performance reached a Pearson coefficient correlation ( C C p ) of 0.8616 and a root mean square error (RMSE) of 2.94e-6 in predicting drug responses for Lung Adenocarcinoma (LUAD) cell lines. Subsequent testing on the Lung Squamous Cell Carcinoma (LUSC) dataset reveals lower performance ( C C p : 0.7999, RMSE: 4.08e-6), attributed to the dataset's smaller size influencing model capacity. Moreover, we assessed the model's architecture by isolating its components, with results indicating that the global network is particularly effective in this task. In conclusion, MLG2Net exhibited promising applications in DRP for cancer cell lines, with potential advancements by incorporating larger datasets. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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