{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T13:35:34Z","timestamp":1769607334469,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T00:00:00Z","timestamp":1722211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Center for Computation and Technology at Louisiana State University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>SynerGNet is a novel approach to predicting drug synergy against cancer cell lines. In this study, we discuss in detail the construction process of SynerGNet, emphasizing its comprehensive design tailored to handle complex data patterns. Additionally, we investigate a counterintuitive phenomenon when integrating more augmented data into the training set results in an increase in testing loss alongside improved predictive accuracy. This sheds light on the nuanced dynamics of model learning. Further, we demonstrate the effectiveness of strong regularization techniques in mitigating overfitting, ensuring the robustness and generalization ability of SynerGNet. Finally, the continuous performance enhancements achieved through the integration of augmented data are highlighted. By gradually increasing the amount of augmented data in the training set, we observe substantial improvements in model performance. For instance, compared to models trained exclusively on the original data, the integration of the augmented data can lead to a 5.5% increase in the balanced accuracy and a 7.8% decrease in the false positive rate. Through rigorous benchmarks and analyses, our study contributes valuable insights into the development and optimization of predictive models in biomedical research.<\/jats:p>","DOI":"10.3390\/make6030087","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T18:04:59Z","timestamp":1722535499000},"page":"1782-1797","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Insights from Augmented Data Integration and Strong Regularization in Drug Synergy Prediction with SynerGNet"],"prefix":"10.3390","volume":"6","author":[{"given":"Mengmeng","family":"Liu","sequence":"first","affiliation":[{"name":"Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA"}]},{"given":"Gopal","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4349-1327","authenticated-orcid":false,"given":"J.","family":"Ramanujam","sequence":"additional","affiliation":[{"name":"Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA"},{"name":"Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6204-2869","authenticated-orcid":false,"given":"Michal","family":"Brylinski","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA"},{"name":"Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1038\/nrm810","article-title":"The rise of computational biology","volume":"3","author":"Noble","year":"2002","journal-title":"Nat. Rev. Mol. Cell Biol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Markowetz, F. (2017). All biology is computational biology. PLoS Biol., 15.","DOI":"10.1371\/journal.pbio.2002050"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Caragea, C., and Honavar, V.G. (2009). Machine Learning in Computational Biology, Springer.","DOI":"10.1007\/978-0-387-39940-9_636"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chicco, D. (2017). Ten quick tips for machine learning in computational biology. BioData Min., 10.","DOI":"10.1186\/s13040-017-0155-3"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tarca, A.L., Carey, V.J., Chen, X.-W., Romero, R., and Dr\u0103ghici, S. (2007). Machine learning and its applications to biology. PLoS Comput. Biol., 3.","DOI":"10.1371\/journal.pcbi.0030116"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Angermueller, C., P\u00e4rnamaa, T., Parts, L., and Stegle, O. (2016). Deep learning for computational biology. Mol. Syst. Biol., 12.","DOI":"10.15252\/msb.20156651"},{"key":"ref_7","first-page":"20140081","article-title":"Machine learning methods in the computational biology of cancer","volume":"470","author":"Vidyasagar","year":"2014","journal-title":"Proc. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1042\/ETLS20160025","article-title":"Computational biology: Deep learning","volume":"1","author":"Jones","year":"2017","journal-title":"Emerg. Top. Life Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wu, L., Wen, Y., Leng, D., Zhang, Q., Dai, C., Wang, Z., Liu, Z., Yan, B., Zhang, Y., and Wang, J. (2022). Machine learning methods, databases and tools for drug combination prediction. Brief. Bioinf., 23.","DOI":"10.1093\/bib\/bbab355"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2674","DOI":"10.1038\/s41467-019-09799-2","article-title":"Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen","volume":"10","author":"Menden","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cmpb.2018.11.002","article-title":"Predicting combinative drug pairs via multiple classifier system with positive samples only","volume":"168","author":"Shi","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.cels.2015.12.003","article-title":"Prediction of synergism from chemical-genetic interactions by machine learning","volume":"1","author":"Wildenhain","year":"2015","journal-title":"Cell Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Torkamannia, A., Omidi, Y., and Ferdousi, R. (2022). A review of machine learning approaches for drug synergy prediction in cancer. Brief. Bioinf., 23.","DOI":"10.1093\/bib\/bbac075"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1093\/bioinformatics\/btx806","article-title":"DeepSynergy: Predicting anti-cancer drug synergy with Deep Learning","volume":"34","author":"Preuer","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4483","DOI":"10.1093\/bioinformatics\/btaa287","article-title":"DTF: Deep tensor factorization for predicting anticancer drug synergy","volume":"36","author":"Sun","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_16","first-page":"223","article-title":"Synergistic drug combination prediction by integrating multiomics data in deep learning models","volume":"2194","author":"Zhang","year":"2021","journal-title":"Transl. Bioinf. Ther. Dev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1158\/1541-7786.MCR-21-0735","article-title":"SynPathy: Predicting drug synergy through drug-associated pathways using deep learning","volume":"20","author":"Tang","year":"2022","journal-title":"Mol. Cancer Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5975","DOI":"10.1007\/s10462-022-10306-1","article-title":"Deep learning in drug discovery: An integrative review and future challenges","volume":"56","author":"Askr","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2336","DOI":"10.1093\/jamia\/ocab162","article-title":"GraphSynergy: A network-inspired deep learning model for anticancer drug combination prediction","volume":"28","author":"Yang","year":"2021","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, J., Liu, X., Shen, S., Deng, L., and Liu, H. (2022). DeepDDS: Deep graph neural network with attention mechanism to predict synergistic drug combinations. Brief. Bioinf., 23.","DOI":"10.1093\/bib\/bbab390"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2854","DOI":"10.1021\/acs.jcim.3c00709","article-title":"AttenSyn: An attention-based deep graph neural network for anticancer synergistic drug combination prediction","volume":"64","author":"Wang","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, M., Srivastava, G., Ramanujam, J., and Brylinski, M. (2024). SynerGNet: A Graph Neural Network Model to Predict Anticancer Drug Synergy. Biomolecules, 14.","DOI":"10.3390\/biom14030253"},{"key":"ref_23","first-page":"D871","article-title":"DrugCombDB: A comprehensive database of drug combinations toward the discovery of combinatorial therapy","volume":"48","author":"Liu","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhu, H., Jiang, Y., Li, Y., Tang, C., Chen, X., Li, Y., and Liu, Q. (2022). PRODeepSyn: Predicting anticancer synergistic drug combinations by embedding cell lines with protein\u2013protein interaction network. Brief. Bioinf., 23.","DOI":"10.1093\/bib\/bbab587"},{"key":"ref_25","unstructured":"Bjerrum, E.J. (2017). SMILES enumeration as data augmentation for neural network modeling of molecules. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sidorov, P., Naulaerts, S., Ariey-Bonnet, J., Pasquier, E., and Ballester, P.J. (2019). Predicting synergism of cancer drug combinations using NCI-ALMANAC data. Front. Chem., 7.","DOI":"10.3389\/fchem.2019.00509"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2102092","DOI":"10.1002\/advs.202102092","article-title":"ScaffComb: A Phenotype-Based Framework for Drug Combination Virtual Screening in Large-Scale Chemical Datasets","volume":"8","author":"Ye","year":"2021","journal-title":"Adv. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1668","DOI":"10.1038\/s41598-024-51940-9","article-title":"Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects","volume":"14","author":"Liu","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3510413","article-title":"Avoiding overfitting: A survey on regularization methods for convolutional neural networks","volume":"54","author":"Santos","year":"2022","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Aghajanyan, A. (2017, January 21\u201323). Softtarget regularization: An effective technique to reduce over-fitting in neural networks. Proceedings of the 2017 3rd IEEE International Conference on Cybernetics (CYBCONF), Exeter, UK.","DOI":"10.1109\/CYBConf.2017.7985811"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ying, X. (2019). An Overview of Overfitting and Its Solutions, IOP Publishing.","DOI":"10.1088\/1742-6596\/1168\/2\/022022"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"D380","DOI":"10.1093\/nar\/gkv1277","article-title":"STITCH 5: Augmenting protein\u2013chemical interaction networks with tissue and affinity data","volume":"44","author":"Szklarczyk","year":"2016","journal-title":"Nucleic Acids Res."},{"key":"ref_33","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","unstructured":"Brody, S., Alon, U., and Yahav, E. (2021). How attentive are graph attention networks?. arXiv."},{"key":"ref_35","unstructured":"Xu, K., Hu, W., Leskovec, J., and Jegelka, S. (2018). How powerful are graph neural networks?. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Shi, Y., Huang, Z., Feng, S., Zhong, H., Wang, W., and Sun, Y. (2020). Masked label prediction: Unified message passing model for semi-supervised classification. arXiv.","DOI":"10.24963\/ijcai.2021\/214"},{"key":"ref_37","unstructured":"Li, G., Xiong, C., Thabet, A., and Ghanem, B. (2020). Deepergcn: All you need to train deeper gcns. arXiv."},{"key":"ref_38","first-page":"12","article-title":"The reduction of a graph to canonical form and the algebra which appears therein","volume":"2","author":"Weisfeiler","year":"1968","journal-title":"Nti Ser."},{"key":"ref_39","unstructured":"Vinyals, O., Bengio, S., and Kudlur, M. (2015). Order matters: Sequence to sequence for sets. arXiv."},{"key":"ref_40","first-page":"4202","article-title":"Understanding attention and generalization in graph neural networks","volume":"32","author":"Knyazev","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_41","first-page":"16421","article-title":"Path integral based convolution and pooling for graph neural networks","volume":"33","author":"Ma","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_42","unstructured":"Ahmadi, A.H.K. (2020). Memory-Based Graph Networks, University of Toronto."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Mazandu, G.K., Hooper, C., Opap, K., Makinde, F., Nembaware, V., Thomford, N.E., and Mulder, N.J. (2021). IHP-PING\u2014Generating integrated human protein\u2013protein interaction networks on-the-fly. Brief. Bioinf., 22.","DOI":"10.1093\/bib\/bbaa277"},{"key":"ref_44","unstructured":"Soltius (2024, February 22). How Is It Possible That Validation Loss Is Increasing While Validation Accuracy Is Increasing as Well. Available online: https:\/\/stats.stackexchange.com\/q\/341054."},{"key":"ref_45","unstructured":"Kim, D., and Oh, A. (2022). How to find your friendly neighborhood: Graph attention design with self-supervision. arXiv."},{"key":"ref_46","unstructured":"Tailor, S.A., Opolka, F.L., Lio, P., and Lane, N.D. (2021). Do we need anisotropic graph neural networks?. arXiv."},{"key":"ref_47","unstructured":"Zhu, H., and Koniusz, P. (2021, January 3\u20137). Simple spectral graph convolution. Proceedings of the International Conference on Learning Representations, Virtual Event."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/6\/3\/87\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:25:50Z","timestamp":1760109950000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/6\/3\/87"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,29]]},"references-count":47,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["make6030087"],"URL":"https:\/\/doi.org\/10.3390\/make6030087","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,29]]}}}