{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:18:43Z","timestamp":1761175123914,"version":"build-2065373602"},"reference-count":20,"publisher":"Polish Information Processing Society","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"DOI":"10.15439\/2025f1171","type":"proceedings-article","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T08:05:47Z","timestamp":1761120347000},"page":"93-97","source":"Crossref","is-referenced-by-count":0,"title":["Multitask Learning for Six-Pack Toxicity Prediction"],"prefix":"10.15439","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3011-8440","authenticated-orcid":true,"given":"Chun-Wei","family":"Tung","sequence":"first","affiliation":[{"name":"Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1272-1513","authenticated-orcid":true,"given":"Chia-Chi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department and Graduate Institute of Veterinary Medicine, School of Veterinary Medicine, National Taiwan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1205-2874","authenticated-orcid":true,"given":"Run-Hsin","family":"Lin","sequence":"additional","affiliation":[{"name":"National Health Research Institutes"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6163-120X","authenticated-orcid":true,"given":"Shan-Shan","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"6175","published-online":{"date-parts":[[2025,10,15]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","unstructured":"J. V. B. Borba et al., \u201cSTopTox: An in Silico Alternative to Animal Testing for Acute Systemic and Topical Toxicity,\u201d Environ. Health Perspect., vol. 130, no. 2, p. 27012, Feb. 2022, https:\/\/dx.doi.org\/10.1289\/EHP9341.","DOI":"10.1289\/EHP9341"},{"key":"ref2","doi-asserted-by":"publisher","unstructured":"Y. N. Fuadah, M. A. Pramudito, L. Firdaus, F. J. Vanheusden, and K. M. Lim, \u201cQSAR Classification Modeling Using Machine Learning with a Consensus-Based Approach for Multivariate Chemical Hazard End Points,\u201d ACS Omega, vol. 9, no. 51, pp. 50796\u201350808, Dec. 2024, https:\/\/dx.doi.org\/10.1021\/acsomega.4c09356.","DOI":"10.1021\/acsomega.4c09356"},{"key":"ref3","doi-asserted-by":"publisher","unstructured":"Y. Chushak, J. M. Gearhart, and R. A. Clewell, \u201cStructural alerts and Machine learning modeling of \u2018Six-pack\u2019 toxicity as alternative to animal testing,\u201d Comput. Toxicol., vol. 27, p. 100280, Aug. 2023, https:\/\/dx.doi.org\/10.1016\/j.comtox.2023.100280.","DOI":"10.1016\/j.comtox.2023.100280"},{"key":"ref4","doi-asserted-by":"publisher","unstructured":"B. Ramsundar et al., \u201cIs Multitask Deep Learning Practical for Pharma?,\u201d J. Chem. Inf. Model., vol. 57, no. 8, pp. 2068\u20132076, Aug. 2017, https:\/\/dx.doi.org\/10.1021\/acs.jcim.7b00146.","DOI":"10.1021\/acs.jcim.7b00146"},{"key":"ref5","doi-asserted-by":"publisher","unstructured":"N. Erickson et al., \u201cAutoGluon-Tabular: Robust and Accurate AutoML for Structured Data,\u201d Mar. 13, 2020, https:\/\/arxiv.org\/abs\/ arXiv:2003.06505. https:\/\/dx.doi.org\/10.48550\/arXiv.2003.06505.","DOI":"10.48550\/arXiv.2003.06505"},{"key":"ref6","doi-asserted-by":"publisher","unstructured":"Z. Tan, Y. Li, W. Shi, and S. Yang, \u201cA Multitask Approach to Learn Molecular Properties,\u201d J. Chem. Inf. Model., vol. 61, no. 8, pp. 3824\u20133834, Aug. 2021, https:\/\/dx.doi.org\/10.1021\/acs.jcim.1c00646.","DOI":"10.1021\/acs.jcim.1c00646"},{"key":"ref7","doi-asserted-by":"publisher","unstructured":"X. Qian et al., \u201cAn Interpretable Multitask Framework BiLAT Enables Accurate Prediction of Cyclin-Dependent Protein Kinase Inhibitors,\u201d J. Chem. Inf. Model., vol. 63, no. 11, pp. 3350\u20133368, Jun. 2023, https:\/\/dx.doi.org\/10.1021\/acs.jcim.3c00473.","DOI":"10.1021\/acs.jcim.3c00473"},{"key":"ref8","doi-asserted-by":"publisher","unstructured":"Y. Yuan Li et al., \u201cCo-model for chemical toxicity prediction based on multi-task deep learning,\u201d Mol. Inform., vol. 42, no. 5, p. e2200257, May 2023, https:\/\/dx.doi.org\/10.1002\/minf.202200257.","DOI":"10.1002\/minf.202200257"},{"key":"ref9","doi-asserted-by":"publisher","unstructured":"X. Lin, Z. Quan, Z.-J. Wang, H. Huang, and X. Zeng, \u201cA novel molecular representation with BiGRU neural networks for learning atom,\u201d Brief. Bioinform., vol. 21, no. 6, pp. 2099\u20132111, Dec. 2020, https:\/\/dx.doi.org\/10.1093\/bib\/bbz125.","DOI":"10.1093\/bib\/bbz125"},{"key":"ref10","doi-asserted-by":"publisher","unstructured":"Y. Wang et al., \u201cMultitask CapsNet: An Imbalanced Data Deep Learning Method for Predicting Toxicants,\u201d ACS Omega, vol. 6, no. 40, pp. 26545\u201326555, Oct. 2021, https:\/\/dx.doi.org\/10.1021\/acsomega.1c03842.","DOI":"10.1021\/acsomega.1c03842"},{"key":"ref11","doi-asserted-by":"publisher","unstructured":"R.-H. Lin, P. Lin, C.-C. Wang, and C.-W. Tung, \u201cA novel multitask learning algorithm for tasks with distinct chemical space: zebrafish toxicity prediction as an example,\u201d J. Cheminformatics, vol. 16, no. 1, p. 91, Aug. 2024, https:\/\/dx.doi.org\/10.1186\/s13321-024-00891-4.","DOI":"10.1186\/s13321-024-00891-4"},{"key":"ref12","doi-asserted-by":"publisher","unstructured":"L. Breiman, \u201cRandom Forests,\u201d Mach. Learn., vol. 45, no. 1, pp. 5\u201332, Oct. 2001, https:\/\/dx.doi.org\/10.1023\/A:1010933404324.","DOI":"10.1023\/A:1010933404324"},{"key":"ref13","doi-asserted-by":"publisher","unstructured":"C.-C. Wang et al., \u201cUsing random forest to predict antimicrobial minimum inhibitory concentrations of nontyphoidal Salmonella in Taiwan,\u201d Vet. Res., vol. 54, no. 1, p. 11, Feb. 2023, https:\/\/dx.doi.org\/10.1186\/s13567-023-01141-5.","DOI":"10.1186\/s13567-023-01141-5"},{"key":"ref14","doi-asserted-by":"publisher","unstructured":"C.-Y. Chou, P. Lin, J. Kim, S.-S. Wang, C.-C. Wang, and C.-W. Tung, \u201cEnsemble learning for predicting ex vivo human placental barrier permeability,\u201d BMC Bioinformatics, vol. 22, no. Suppl 10, p. 629, Sep. 2022, https:\/\/dx.doi.org\/10.1186\/s12859-022-04937-y.","DOI":"10.1186\/s12859-022-04937-y"},{"key":"ref15","doi-asserted-by":"publisher","unstructured":"C.-C. Wang, Y.-C. Liang, S.-S. Wang, P. Lin, and C.-W. Tung, \u201cA machine learning-driven approach for prioritizing food contact chemicals of carcinogenic concern based on complementary in silico methods,\u201d Food Chem. Toxicol. Int. J. Publ. Br. Ind. Biol. Res. Assoc., vol. 160, p. 112802, Feb. 2022, https:\/\/dx.doi.org\/10.1016\/j.fct.2021.112802.","DOI":"10.1016\/j.fct.2021.112802"},{"key":"ref16","doi-asserted-by":"publisher","unstructured":"H.-L. Lin, Y.-W. Chiu, C.-C. Wang, and C.-W. Tung, \u201cComputational prediction of Calu-3-based in vitro pulmonary permeability of chemicals,\u201d Regul. Toxicol. Pharmacol. RTP, vol. 135, p. 105265, Nov. 2022, https:\/\/dx.doi.org\/10.1016\/j.yrtph.2022.105265.","DOI":"10.1016\/j.yrtph.2022.105265"},{"key":"ref17","doi-asserted-by":"publisher","unstructured":"V. Svetnik, A. Liaw, C. Tong, J. C. Culberson, R. P. Sheridan, and B. P. Feuston, \u201cRandom forest: a classification and regression tool for compound classification and QSAR modeling,\u201d J. Chem. Inf. Comput. Sci., vol. 43, no. 6, pp. 1947\u20131958, 2003, https:\/\/dx.doi.org\/10.1021\/ci034160g.","DOI":"10.1021\/ci034160g"},{"key":"ref18","doi-asserted-by":"publisher","unstructured":"C.-C. Wang, Y.-C. Lin, Y.-C. Lin, S.-R. Jhang, and C.-W. Tung, \u201cIdentification of informative features for predicting proinflammatory potentials of engine exhausts,\u201d Biomed. Eng. Online, vol. 16, no. Suppl 1, p. 66, Aug. 2017, https:\/\/dx.doi.org\/10.1186\/s12938-017-0355-6.","DOI":"10.1186\/s12938-017-0355-6"},{"key":"ref19","doi-asserted-by":"publisher","unstructured":"Y. Low et al., \u201cIntegrative chemical-biological read-across approach for chemical hazard classification,\u201d Chem. Res. Toxicol., vol. 26, no. 8, pp. 1199\u20131208, Aug. 2013, https:\/\/dx.doi.org\/10.1021\/tx400110f.","DOI":"10.1021\/tx400110f"},{"key":"ref20","doi-asserted-by":"publisher","unstructured":"Y. Guo, L. Zhao, X. Zhang, and H. Zhu, \u201cUsing a hybrid read-across method to evaluate chemical toxicity based on chemical structure and biological data,\u201d Ecotoxicol. Environ. Saf., vol. 178, pp. 178\u2013187, Aug. 2019, https:\/\/dx.doi.org\/10.1016\/j.ecoenv.2019.04.019.","DOI":"10.1016\/j.ecoenv.2019.04.019"}],"event":{"name":"20th Conference on Computer Science and Intelligence Systems","theme":"Computer Science and Intelligence Systems","location":"Krak\u00f3w, Poland","acronym":"FedCSIS","number":"20","start":{"date-parts":[[2025,11,14]]},"end":{"date-parts":[[2025,11,17]]}},"container-title":["Annals of Computer Science and Information Systems","Position Papers of the 20th Conference on Computer Science and Intelligence Systems"],"original-title":[],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T08:07:15Z","timestamp":1761120435000},"score":1,"resource":{"primary":{"URL":"https:\/\/annals-csis.org\/Volume_44\/drp\/1171.html"}},"subtitle":[],"proceedings-subject":"Computer Science and Information Systems","short-title":[],"issued":{"date-parts":[[2025,10,15]]},"references-count":20,"URL":"https:\/\/doi.org\/10.15439\/2025f1171","relation":{},"ISSN":["2300-5963"],"issn-type":[{"value":"2300-5963","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10,15]]}}}