MLG2Net: Molecular Global Graph Network for Drug Response Prediction in Lung Cancer Cell Lines

Meyer, U. A., Zanger, U. M., and Schwab, M., Omics and drug response. Annu. Rev. Pharmacol. Toxicol. 53, 475–502, 2013.

Article  PubMed  Google Scholar 

Feng, F., Shen, B., Mou, X., Li, Y., and Li, H., Large-scale pharmacogenomic studies and drug response prediction for personalized cancer medicine. J. Genet. Genomics 48(7), 540–551, 2021

Article  PubMed  Google Scholar 

Weinshilboum, R. M., and Wang, L., Pharmacogenomics: precision medicine and drug response. In: Mayo Clinic Proceedings. Vol. 92, pp. 1711–1722. Elsevier, 2017.

Wheeler, H. E., Maitland, M. L., Dolan, M. E., Cox, N. J., and Ratain, M. J., Cancer pharmacogenomics: strategies and challenges. Nat. Rev. Genet. 14(1), 23–34, 2013.

Article  PubMed  Google Scholar 

Yang, W., Soares, J., Greninger, P., Edelman, E. J., Lightfoot, H., Forbes, S., Bindal, N., Beare, D., Smith, J. A., Thompson, I. R., et al., Genomics of drug sensitivity in cancer (gdsc): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 41(D1), 955–961, 2012.

Article  Google Scholar 

Workman, P., The nci-60 human tumor cell line screen: A catalyst for progressive evolution of models for discovery and development of cancer drugs. Cancer Res. 83(19), 3170–3173, 2023.

Article  PubMed  Google Scholar 

Rees, M. G., Seashore-Ludlow, B., Cheah, J. H., Adams, D. J., Price, E. V., Gill, S., Javaid, S., Coletti, M. E., Jones, V. L., Bodycombe, N. E., et al., Correlating chemical sensitivity and basal gene expression reveals mechanism of action. Nat. Chem. Biol. 12(2), 109–116, 2016.

Article  PubMed  Google Scholar 

Smirnov, P., Kofia, V., Maru, A., Freeman, M., Ho, C., El-Hachem, N., Adam, G. -A., Ba-Alawi, W., Safikhani, Z., and Haibe-Kains, B., Pharmacodb: an integrative database for mining in vitro anticancer drug screening studies. Nucleic Acids Res. 46(D1), 994–1002, 2018

Article  Google Scholar 

Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al., The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483(7391), 603–607, 2012.

Article  PubMed  PubMed Central  Google Scholar 

Su, R., Liu, X., Xiao, G., and Wei, L., Meta-gdbp: a high-level stacked regression model to improve anticancer drug response prediction. Brief. Bioinform. 21(3), 996–1005, 2020.

Article  PubMed  Google Scholar 

Nguyen, L., Nguyen Vo, T. -H., Trinh, Q. H., Nguyen, B. H., Nguyen-Hoang, P. -U., Le, L., and Nguyen, B. P., ianp-ec: identifying anticancer natural products using ensemble learning incorporated with evolutionary computation. J. Chem. Inf. Model. 62(21), 5080–5089, 2022.

Article  PubMed  Google Scholar 

Zhou, J. -B., Tang, D., He, L., Lin, S., Lei, J. H., Sun, H., Xu, X., and Deng, C. -X., Machine learning model for anti-cancer drug combinations: Analysis, prediction, and validation. Pharmacol. Res. 194, 106830, 2023.

Article  PubMed  Google Scholar 

Nguyen-Vo, T. -H., Do, T. T., and Nguyen, B. P., Multitask learning on graph convolutional residual neural networks for screening of multitarget anticancer compounds. J. Chem. Inf. Model. 64(18), 6957–6968, 2024.

Article  PubMed  Google Scholar 

Li, M., Wang, Y., Zheng, R., Shi, X., Li, Y., Wu, F. -X., Wang, J., Deepdsc: a deep learning method to predict drug sensitivity of cancer cell lines. IEEE/ACM Trans. Comput. Biol. Bioinform. 18(2), 575–582, 2019.

Article  Google Scholar 

Gerdes, H., Casado, P., Dokal, A., Hijazi, M., Akhtar, N., Osuntola, R., Rajeeve, V., Fitzgibbon, J., Travers, J., Britton, D., et al., Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs. Nat. Commun. 12(1), 1850, 2021.

Google Scholar 

Chang, Y., Park, H., Yang, H. -J., Lee, S., Lee, K. -Y., Kim, T. S., Jung, J., and Shin, J. -M., Cancer drug response profile scan (cdrscan): a deep learning model that predicts drug effectiveness from cancer genomic signature. Scientific Reports 8(1), 8857, 2018.

Article  PubMed  PubMed Central  Google Scholar 

Zuo, Z., Wang, P., Chen, X., Tian, L., Ge, H., and Qian, D., Swnet: a deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures. BMC Bioinform. 22, 1–16, 2021.

Article  Google Scholar 

Baptista, D., Ferreira, P. G., and Rocha, M., Deep learning for drug response prediction in cancer. Brief. Bioinform. 22(1), 360–379, 2021.

Article  PubMed  Google Scholar 

An, X., Chen, X., Yi, D., Li, H., and Guan, Y., Representation of molecules for drug response prediction. Brief. Bioinform. 23(1), 393, 2022.

Article  Google Scholar 

Nguyen, T., Le, H., Quinn, T. P., Nguyen, T., Le, T. D., and Venkatesh, S., Graphdta: predicting drug–target binding affinity with graph neural networks. Bioinformatics 37(8), 1140–1147, 2021

Nguyen, T., Nguyen, G. T., Nguyen, T., and Le, D. -H., Graph convolutional networks for drug response prediction. IEEE/ACM Trans. Comput. Biol. Bioinforma. 19(1), 146–154, 2021.

Article  Google Scholar 

Siegel, R. L., Miller, K. D., Wagle, N. S., and Jemal, A., Cancer statistics, 2023. Ca. Cancer J. Clin. 73(1), 17–48, 2023.

Article  PubMed  Google Scholar 

Kim, S., Exploring chemical information in pubchem. Curr. Protoc. 1(8), 217, 2021.

Article  Google Scholar 

Bento, A. P., Hersey, A., Félix, E., Landrum, G., Gaulton, A., Atkinson, F., Bellis, L. J., De Veij, M., and Leach, A. R., An open source chemical structure curation pipeline using rdkit. J. Cheminformatics 12, 1–16, 2020.

Article  Google Scholar 

Ramsundar, B., Molecular machine learning with deepchem. PhD thesis, Stanford University, 2018.

Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y., Graph attention networks. arXiv preprint arXiv:1710.10903 2017.

Xu, J., Li, Z., Du, B., Zhang, M., and Liu, J., Reluplex made more practical: Leaky relu. In: 2020 IEEE Symposium on Computers and Communications (ISCC). pp. 1–7. IEEE, 2020.

Khajenezhad, A., Osia, S. A., Karimian, M., and Beigy, H., Gransformer: Transformer-based graph generation. arXiv preprint arXiv:2203.13655 2022.

Vo, T. H., Nguyen, N. T. K., and Le, N. Q. K., Improved prediction of drug-drug interactions using ensemble deep neural networks. Med. Drug Discov. 17, 100149, 2023.

Article  Google Scholar 

Kha, Q. -H., Tran, T. -O., Nguyen, V. -N., Than, K., Le, N. Q. K., et al., An interpretable deep learning model for classifying adaptor protein complexes from sequence information. Methods 207, 90–96, 2022.

Article  PubMed  Google Scholar 

Li, B., Dai, C., Wang, L., Deng, H., Li, Y., Guan, Z., Ni, H., A novel drug repurposing approach for non-small cell lung cancer using deep learning. PLoS One 15(6), 0233112, 2020.

Article  Google Scholar 

Wessolly, M., Stephan-Falkenau, S., Streubel, A., Werner, R., Borchert, S., Griff, S., Mairinger, E., Walter, R. F., Bauer, T., Eberhardt, W. E., et al., A novel epitope quality-based immune escape mechanism reveals patient’s suitability for immune checkpoint inhibition. Cancer Manag. Res. 7881–7890, 2020.

Ahmed, K. T., Park, S., Jiang, Q., Yeu, Y., Hwang, T., and Zhang, W., Network-based drug sensitivity prediction. BMC Med. Genet. 13(11), 1–10, 2020.

Google Scholar 

Comments (0)

No login
gif