{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:13:59Z","timestamp":1761808439731,"version":"3.41.0"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030779665"},{"type":"electronic","value":"9783030779672"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-77967-2_40","type":"book-chapter","created":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T13:25:58Z","timestamp":1623331558000},"page":"479-493","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Hybrid Predictive Modelling for Finding Optimal Multipurpose Multicomponent Therapy"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1690-9812","authenticated-orcid":false,"given":"Vladislav V.","family":"Pavlovskii","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8624-5046","authenticated-orcid":false,"given":"Ilia V.","family":"Derevitskii","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8828-4615","authenticated-orcid":false,"given":"Sergey V.","family":"Kovalchuk","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,9]]},"reference":[{"key":"40_CR1","doi-asserted-by":"crossref","unstructured":"Dedov, I.I., et al.: Standards of specialized diabetes care. In: Dedov, I.I., Shestakova M.V., Mayorov A.Yu Diabetes Mellit, 9th ed. (2019)","DOI":"10.14341\/DM221S1"},{"key":"40_CR2","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1016\/j.ccc.2009.08.004","volume":"25","author":"A Kumar","year":"2009","unstructured":"Kumar, A.: Optimizing antimicrobial therapy in sepsis and septic shock. Crit. Care Clin. 25, 733\u2013751 (2009)","journal-title":"Crit. Care Clin."},{"key":"40_CR3","first-page":"744","volume":"25","author":"P Srividya","year":"2012","unstructured":"Srividya, P., Devi, T.S.R., Gunasekaran, S.: Ftir spectral study on diabetic blood samples \u2013 monotherapy and combination therapy. Ojp 25, 744\u2013750 (2012)","journal-title":"Ojp"},{"key":"40_CR4","first-page":"289","volume":"30","author":"JK Sicklick","year":"2019","unstructured":"Sicklick, J.K., et al.: Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study. Nat. Med. 30, 289\u2013299 (2019)","journal-title":"Nat. Med."},{"key":"40_CR5","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1111\/j.1464-5491.2012.03746.x","volume":"30","author":"M Burgmaier","year":"2013","unstructured":"Burgmaier, M., Heinrich, C., Marx, N.: Cardiovascular effects of GLP-1 and GLP-1-based therapies: implications for the cardiovascular continuum in diabetes? Diab. Med. 30, 289\u2013299 (2013)","journal-title":"Diab. Med."},{"key":"40_CR6","doi-asserted-by":"publisher","first-page":"e61318","DOI":"10.1371\/journal.pone.0061318","volume":"8","author":"MP Menden","year":"2013","unstructured":"Menden, M.P., et al.: Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties. PLoS One 8, e61318 (2013)","journal-title":"PLoS One"},{"key":"40_CR7","doi-asserted-by":"publisher","first-page":"e10264","DOI":"10.15252\/emmm.201910264","volume":"12","author":"A Khaledi","year":"2020","unstructured":"Khaledi, A., et al.: Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics. EMBO Mol. Med. 12, e10264 (2020)","journal-title":"EMBO Mol. Med."},{"key":"40_CR8","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.compbiomed.2015.03.019","volume":"61","author":"C Barbieri","year":"2015","unstructured":"Barbieri, C., et al.: A new machine learning approach for predicting the response to anemia treatment in a large cohort of End Stage Renal Disease patients undergoing dialysis. Comput. Biol. Med. 61, 56\u201361 (2015)","journal-title":"Comput. Biol. Med."},{"key":"40_CR9","doi-asserted-by":"crossref","unstructured":"Janizek, J.D., Celik, S., Lee, S.I.: Explainable machine learning prediction of synergistic drug combinations for precision cancer medicine. bioRxiv 8, 1-115 (2018)","DOI":"10.1101\/331769"},{"issue":"9","key":"40_CR10","doi-asserted-by":"publisher","first-page":"1538","DOI":"10.1093\/bioinformatics\/btx806","volume":"34","author":"K Preuer","year":"2018","unstructured":"Preuer, K., Lewis, R., Hochreiter, S., Bender, A., Bulusu, K., Klambauer, G.: DeepSynergy: predicting anti-cancer drug synergy with Deep Learning. Bioinformatics 34(9), 1538\u20131546 (2018)","journal-title":"Bioinformatics"},{"key":"40_CR11","doi-asserted-by":"publisher","first-page":"117693431879026","DOI":"10.1177\/1176934318790266","volume":"14","author":"A Tabl","year":"2018","unstructured":"Tabl, A., Alkhateeb, A., Pham, H., Rueda, L., ElMaraghy, W., Ngom, A.: A novel approach for identifying relevant genes for breast cancer survivability on specific therapies. Evol. Bioinf. 14, 117693431879026 (2018)","journal-title":"Evol. Bioinf."},{"issue":"5","key":"40_CR12","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1016\/j.ccell.2020.09.014","volume":"38","author":"B Kuenzi","year":"2020","unstructured":"Kuenzi, B., et al.: Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer Cell 38(5), 672-684.e6 (2020). https:\/\/doi.org\/10.1016\/j.ccell.2020.09.014","journal-title":"Cancer Cell"},{"key":"40_CR13","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1111\/dom.14042","volume":"22","author":"J Butler","year":"2020","unstructured":"Butler, J., Januzzi, J.L., Rosenstock, J.: Management of heart failure and type 2 diabetes mellitus: Maximizing complementary drug therapy. Diab. Obes. Metab. 22, 1243\u20131262 (2020)","journal-title":"Diab. Obes. Metab."},{"issue":"1","key":"40_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-015-0069-3","volume":"7","author":"D Bajusz","year":"2015","unstructured":"Bajusz, D., R\u00e1cz, A., H\u00e9berger, K.: Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J. Cheminf. 7(1), 1\u201313 (2015)","journal-title":"J. Cheminf."}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-77967-2_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T22:05:10Z","timestamp":1749506710000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-77967-2_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030779665","9783030779672"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-77967-2_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 June 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Krakow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"156","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"48","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.8","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.9","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"212 full and 43 short papers were selected from 479 submissions to the workshops\/ thematic tracks. The conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}