{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T06:40:50Z","timestamp":1764571250037,"version":"build-2065373602"},"reference-count":23,"publisher":"Walter de Gruyter GmbH","issue":"2","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Predicting Drug-Drug interaction (DDI)-induced adverse drug reactions (ADRs) using computational methods is challenging due to the availability of limited data samples, data sparsity, and high dimensionality. The issue of class imbalance further increases the intricacy of prediction. Different computational techniques have been presented for predicting DDI-induced ADRs in the general population; however, the area of DDI-induced pregnancy and neonatal ADRs has been underexplored. In the present work, a sparse ensemble-based computational approach is proposed that leverages SMILES strings as features, addresses high-dimensional and sparse data using Sparse Principal Component Analysis (SPCA), mitigates class imbalance with the Multilabel Synthetic Minority Oversampling Technique (MLSMOTE), and predicts pregnancy and neonatal ADRs through a stacking ensemble model. The SPCA has been evaluated for handling sparse data and shown 2.67\u202f%\u20135.45\u202f% improvement compared to PCA. The proposed stacking ensemble model has outperformed six state-of-the-art predictors regarding micro and macro scores for True Positive Rate (\n                    <jats:italic>TPR<\/jats:italic>\n                    ), F1 Score, False Positive Rate (\n                    <jats:italic>FPR<\/jats:italic>\n                    ), Precision, Hamming Loss, and ROC-AUC Score with 1.16\u202f%\u201314.94\u202f%.\n                  <\/jats:p>","DOI":"10.1515\/jib-2024-0056","type":"journal-article","created":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T01:55:47Z","timestamp":1749434147000},"source":"Crossref","is-referenced-by-count":1,"title":["Predicting DDI-induced pregnancy and neonatal ADRs using sparse PCA and stacking ensemble approach"],"prefix":"10.1515","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2176-1840","authenticated-orcid":false,"given":"Anushka","family":"Chaurasia","sequence":"first","affiliation":[{"name":"Computer Science and Engineering , 385889 National Institute of Technology Meghalaya , Shillong , India"}]},{"given":"Deepak","family":"Kumar","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering , 385889 National Institute of Technology Meghalaya , Shillong , India"}]},{"family":"Yogita","sequence":"additional","affiliation":[{"name":"Computer Engineering , National Institute of Technology Kurukshetra , Kurukshetra , India"}]}],"member":"374","published-online":{"date-parts":[[2025,6,10]]},"reference":[{"key":"2025102907580410182_j_jib-2024-0056_ref_001","unstructured":"Mair, A, Donaldson, LJ, Kelley, E, Hill, S, Kirke, C, Wilson, M, et al.. Medication safety in polypharmacy: technical report. Geneva, Switzerland: World Health Organization; 2019."},{"key":"2025102907580410182_j_jib-2024-0056_ref_002","doi-asserted-by":"crossref","unstructured":"de Oliveira-Filho, AD, Vieira, AES, da Silva, RC, Neves, SJF, Gama, TAB, Lima, RV, et al.. Adverse drug reactions in high-risk pregnant women: a prospective study. Saudi Pharm J 2017;25:1073\u20137. https:\/\/doi.org\/10.1016\/j.jsps.2017.01.005.","DOI":"10.1016\/j.jsps.2017.01.005"},{"key":"2025102907580410182_j_jib-2024-0056_ref_003","doi-asserted-by":"crossref","unstructured":"Chaurasia, A, Kumar, D, Yogita. InteractNet: improved drug-drug interaction prediction in pharmacology using deep neural networks. In: Practical applications of computational biology & bioinformatics, 18th international conference (PACBB 2024). Springer; 2024.","DOI":"10.1007\/978-3-031-87873-2_1"},{"key":"2025102907580410182_j_jib-2024-0056_ref_004","unstructured":"Office of the Surgeon General. The surgeon general\u2019s call to action to improve maternal health. US Department of Health and Human Services; 2020. Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/33661589\/."},{"key":"2025102907580410182_j_jib-2024-0056_ref_005","doi-asserted-by":"crossref","unstructured":"Anand, A, Phillips, K, Subramanian, A, Lee, SI, Wang, Z, McCowan, R, et al.. Prevalence of polypharmacy in pregnancy: a systematic review. BMJ Open 2023;13:e067585. https:\/\/doi.org\/10.1136\/bmjopen-2022-067585.","DOI":"10.1136\/bmjopen-2022-067585"},{"key":"2025102907580410182_j_jib-2024-0056_ref_006","doi-asserted-by":"crossref","unstructured":"Ragam, JAS, Sheela, S. Prevalence of potential drug-drug interactions among hypertensive pregnant women admitted to a tertiary care hospital. Cureus 2023;15. https:\/\/doi.org\/10.7759\/cureus.36306.","DOI":"10.7759\/cureus.36306"},{"key":"2025102907580410182_j_jib-2024-0056_ref_007","doi-asserted-by":"crossref","unstructured":"Subramanian, A, Azcoaga-Lorenzo, A, Anand, A, Phillips, K, Lee, SI, Cockburn, N, et al.. Polypharmacy during pregnancy and associated risk factors: a retrospective analysis of 577 medication exposures among 1.5 million pregnancies in the UK, 2000\u20132019. BMC Med 2023;21:21. https:\/\/doi.org\/10.1186\/s12916-022-02722-5.","DOI":"10.1186\/s12916-022-02722-5"},{"key":"2025102907580410182_j_jib-2024-0056_ref_008","doi-asserted-by":"crossref","unstructured":"Liu, R, AbdulHameed, MDM, Kumar, K, Yu, X, Wallqvist, A, Reifman, J. Data-driven prediction of adverse drug reactions induced by drug-drug interactions. BMC Pharmacol Toxicol 2017;18:1\u201318. https:\/\/doi.org\/10.1186\/s40360-017-0153-6.","DOI":"10.1186\/s40360-017-0153-6"},{"key":"2025102907580410182_j_jib-2024-0056_ref_009","doi-asserted-by":"crossref","unstructured":"Das, P, Mazumder, DH. An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects. Artif Intell Rev 2023;56:9809\u201336. https:\/\/doi.org\/10.1007\/s10462-023-10413-7.","DOI":"10.1007\/s10462-023-10413-7"},{"key":"2025102907580410182_j_jib-2024-0056_ref_010","doi-asserted-by":"crossref","unstructured":"Vilar, S, Uriarte, E, Santana, L, Tatonetti, NP, Friedman, C. Detection of drug-drug interactions by modeling interaction profile fingerprints. PLoS One 2013;8:e58321. https:\/\/doi.org\/10.1371\/journal.pone.0058321.","DOI":"10.1371\/journal.pone.0058321"},{"key":"2025102907580410182_j_jib-2024-0056_ref_011","doi-asserted-by":"crossref","unstructured":"Raja, K, Patrick, M, Elder, JT, Tsoi, LC. Machine learning workflow to enhance predictions of adverse drug reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases. Sci Rep 2017;7:3690. https:\/\/doi.org\/10.1038\/s41598-017-03914-3.","DOI":"10.1038\/s41598-017-03914-3"},{"key":"2025102907580410182_j_jib-2024-0056_ref_012","doi-asserted-by":"crossref","unstructured":"Zheng, Y, Peng, H, Zhang, X, Zhao, Z, Yin, J, Li, J. Predicting adverse drug reactions of combined medication from heterogeneous pharmacologic databases. BMC Bioinf 2018;19:49\u201359. https:\/\/doi.org\/10.1186\/s12859-018-2520-8.","DOI":"10.1186\/s12859-018-2520-8"},{"key":"2025102907580410182_j_jib-2024-0056_ref_013","doi-asserted-by":"crossref","unstructured":"Zhuang, L, Wang, H, Li, W, Liu, T, Han, S, Zhang, H. MS-ADR: predicting drug\u2013drug adverse reactions base on multi-source heterogeneous convolutional signed network. Soft Comput 2022;26:11795\u2013807. https:\/\/doi.org\/10.1007\/s00500-022-06951-x.","DOI":"10.1007\/s00500-022-06951-x"},{"key":"2025102907580410182_j_jib-2024-0056_ref_014","doi-asserted-by":"crossref","unstructured":"Ibrahim, H, El Kerdawy, AM, Abdo, A, Eldin, AS. Similarity-based machine learning framework for predicting safety signals of adverse drug\u2013drug interactions. Inform Med Unlocked 2021;26:100699. https:\/\/doi.org\/10.1016\/j.imu.2021.100699.","DOI":"10.1016\/j.imu.2021.100699"},{"key":"2025102907580410182_j_jib-2024-0056_ref_015","doi-asserted-by":"crossref","unstructured":"Zhu, J, Liu, Y, Zhang, Y, Chen, Z, She, K, Tong, R. DAEM: deep attributed embedding based multi-task learning for predicting adverse drug\u2013drug interaction. Expert Syst Appl 2023;215:119312. https:\/\/doi.org\/10.1016\/j.eswa.2022.119312.","DOI":"10.1016\/j.eswa.2022.119312"},{"key":"2025102907580410182_j_jib-2024-0056_ref_016","doi-asserted-by":"crossref","unstructured":"Masumshah, R, Eslahchi, C. DPSP: a multimodal deep learning framework for polypharmacy side effects prediction. Bioinform Adv 2023;3:vbad110. https:\/\/doi.org\/10.1093\/bioadv\/vbad110.","DOI":"10.1093\/bioadv\/vbad110"},{"key":"2025102907580410182_j_jib-2024-0056_ref_017","doi-asserted-by":"crossref","unstructured":"Asfand-E-Yar, M, Hashir, Q, Shah, AA, Malik, HAM, Alourani, A, Khalil, W. Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events. Sci Rep 2024;14:4076. https:\/\/doi.org\/10.1038\/s41598-024-54409-x.","DOI":"10.1038\/s41598-024-54409-x"},{"key":"2025102907580410182_j_jib-2024-0056_ref_018","doi-asserted-by":"crossref","unstructured":"Keshavarz, A, Lakizadeh, A. PU-GNN: a positive-unlabeled learning method for polypharmacy side-effects detection based on graph neural networks. Int J Intell Syst 2024;2024:4749668. https:\/\/doi.org\/10.1155\/2024\/4749668.","DOI":"10.1155\/2024\/4749668"},{"key":"2025102907580410182_j_jib-2024-0056_ref_023","doi-asserted-by":"crossref","unstructured":"Zhu, J, Liu, Y, Zhang, Y, Li, D. Attribute supervised probabilistic dependent matrix tri-factorization model for the prediction of adverse drug-drug interaction. IEEE J Biomed Health Inform 2020;25:2820\u201332. https:\/\/doi.org\/10.1109\/JBHI.2020.3048059.","DOI":"10.1109\/JBHI.2020.3048059"},{"key":"2025102907580410182_j_jib-2024-0056_ref_019","doi-asserted-by":"crossref","unstructured":"Kim, S, Thiessen, PA, Bolton, EE, Chen, J, Fu, G, Gindulyte, A, et al.. PubChem substance and compound databases. Nucleic Acids Res 2016;44:D1202\u201313. https:\/\/doi.org\/10.1093\/nar\/gkv951.","DOI":"10.1093\/nar\/gkv951"},{"key":"2025102907580410182_j_jib-2024-0056_ref_020","doi-asserted-by":"crossref","unstructured":"Tatonetti, NP, Ye, PP, Daneshjou, R, Altman, RB. Data-driven prediction of drug effects and interactions. Sci Transl Med 2012;4:125ra31. https:\/\/doi.org\/10.1126\/scitranslmed.30033.","DOI":"10.1126\/scitranslmed.3003377"},{"key":"2025102907580410182_j_jib-2024-0056_ref_021","doi-asserted-by":"crossref","unstructured":"Zou, H, Hastie, T, Tibshirani, R. Sparse principal component analysis. J Comput Graph Stat 2006;15:265\u201386. https:\/\/doi.org\/10.1198\/106186006X113430.","DOI":"10.1198\/106186006X113430"},{"key":"2025102907580410182_j_jib-2024-0056_ref_022","doi-asserted-by":"crossref","unstructured":"Charte, F, Rivera, AJ, del Jesus, MJ, Herrera, F. MLSMOTE: approaching imbalanced multilabel learning through synthetic instance generation. Knowl Base Syst 2015;89:385\u201397. https:\/\/doi.org\/10.1016\/j.knosys.2015.07.019.","DOI":"10.1016\/j.knosys.2015.07.019"}],"container-title":["Journal of Integrative Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jib-2024-0056\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jib-2024-0056\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T07:58:42Z","timestamp":1761724722000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jib-2024-0056\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,1]]},"references-count":23,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,7,14]]},"published-print":{"date-parts":[[2025,10,30]]}},"alternative-id":["10.1515\/jib-2024-0056"],"URL":"https:\/\/doi.org\/10.1515\/jib-2024-0056","relation":{},"ISSN":["1613-4516"],"issn-type":[{"type":"electronic","value":"1613-4516"}],"subject":[],"published":{"date-parts":[[2025,6,1]]},"article-number":"20240056"}}