{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:27:43Z","timestamp":1780392463535,"version":"3.54.1"},"reference-count":89,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T00:00:00Z","timestamp":1741737600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Chronic myeloid leukemia is a clonal hematologic disease characterized by the presence of the Philadelphia chromosome and the BCR::ABL1 fusion protein. Integrating different molecular, genetic, clinical, and laboratory data would improve the diagnostic, prognostic, and predictive sensitivity of chronic myeloid leukemia. However, without artificial intelligence support, managing such a vast volume of data would be impossible. Considering the advancements and growth in machine learning throughout the years, several models and algorithms have been proposed for the management of chronic myeloid leukemia. Here, we provide an overview of recent research that used specific algorithms on patients with chronic myeloid leukemia, highlighting the potential benefits of adopting machine learning in therapeutic contexts as well as its drawbacks. Our analysis demonstrated the great potential for advancing precision treatment in CML through the combination of clinical and genetic data, laboratory testing, and machine learning. We can use these powerful research instruments to unravel the molecular and spatial puzzles of CML by overcoming the current obstacles. A new age of patient-centered hematology care will be ushered in by this, opening the door for improved diagnosis accuracy, sophisticated risk assessment, and customized treatment plans.<\/jats:p>","DOI":"10.3390\/ijms26062535","type":"journal-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T07:31:42Z","timestamp":1741764702000},"page":"2535","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Utilization of Machine Learning in the Prediction, Diagnosis, Prognosis, and Management of Chronic Myeloid Leukemia"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7947-158X","authenticated-orcid":false,"given":"Fabio","family":"Stagno","sequence":"first","affiliation":[{"name":"Division of Hematology, Department of Human Pathology in Adulthood and Childhood \u201cGaetano Barresi\u201d, University of Messina, Via Consolare Valeria, 98125 Messina, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sabina","family":"Russo","sequence":"additional","affiliation":[{"name":"Division of Hematology, Department of Human Pathology in Adulthood and Childhood \u201cGaetano Barresi\u201d, University of Messina, Via Consolare Valeria, 98125 Messina, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6403-6905","authenticated-orcid":false,"given":"Giuseppe","family":"Murdaca","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, University of Genova, 16126 Genova, Italy"},{"name":"Allergology and Clinical Immunology, San Bartolomeo Hospital, 19038 Sarzana, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe","family":"Mirabile","sequence":"additional","affiliation":[{"name":"Division of Hematology, Department of Human Pathology in Adulthood and Childhood \u201cGaetano Barresi\u201d, University of Messina, Via Consolare Valeria, 98125 Messina, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Eugenia","family":"Alvaro","sequence":"additional","affiliation":[{"name":"Division of Hematology, Department of Human Pathology in Adulthood and Childhood \u201cGaetano Barresi\u201d, University of Messina, Via Consolare Valeria, 98125 Messina, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Elisa","family":"Nasso","sequence":"additional","affiliation":[{"name":"Division of Hematology, Department of Human Pathology in Adulthood and Childhood \u201cGaetano Barresi\u201d, University of Messina, Via Consolare Valeria, 98125 Messina, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Zemzem","sequence":"additional","affiliation":[{"name":"Division of Hematology, Department of Human Pathology in Adulthood and Childhood \u201cGaetano Barresi\u201d, University of Messina, Via Consolare Valeria, 98125 Messina, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastiano","family":"Gangemi","sequence":"additional","affiliation":[{"name":"Allergy and Clinical Immunology Unit, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria, 98125 Messina, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6156-8239","authenticated-orcid":false,"given":"Alessandro","family":"Allegra","sequence":"additional","affiliation":[{"name":"Division of Hematology, Department of Human Pathology in Adulthood and Childhood \u201cGaetano Barresi\u201d, University of Messina, Via Consolare Valeria, 98125 Messina, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,12]]},"reference":[{"key":"ref_1","unstructured":"American Cancer Society (2023). Cancer Facts & Figures 2023, American Cancer Society. Available online: https:\/\/www.cancer.org\/content\/dam\/cancer-org\/research\/cancer-facts-and-statistics\/annual-cancer-facts-and-figures\/2023\/2023-cancer-facts-and-figures.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s11899-012-0114-5","article-title":"Genetic mechanisms of chronic myeloid leukemia blastic transformation","volume":"7","author":"Skorski","year":"2012","journal-title":"Curr. Hematol. Malig. Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/0002-9343(87)90754-6","article-title":"Chronic myelogenous leukemia in blast crisis. Analysis of 242 patients","volume":"83","author":"Kantarjian","year":"1987","journal-title":"Am. J. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s11899-015-0249-2","article-title":"Management of advanced-phase chronic myeloid leukemia","volume":"10","author":"DeFilipp","year":"2015","journal-title":"Curr. Hematol. Malig. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11795549221139357","DOI":"10.1177\/11795549221139357","article-title":"Current Management of Chronic Myeloid Leukemia Myeloid Blast Phase","volume":"16","author":"Yohanan","year":"2022","journal-title":"Clin. Med. Insights Oncol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1182\/asheducation-2003.1.132","article-title":"Chronic myeloid leukemia","volume":"2003","author":"Melo","year":"2003","journal-title":"Hematol. Am. Soc. Hematol. Educ. Program"},{"key":"ref_7","first-page":"1517","article-title":"An evidence-based analysis of the effect of busulfan, hydroxyurea, interferon, and allogeneic bone marrow transplantation in treating the chronic phase of chronic myeloid leukemia: Developed for the American Society of Hematology","volume":"94","author":"Silver","year":"1999","journal-title":"Blood"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1038\/s41408-023-00823-9","article-title":"Management of chronic myeloid leukemia in 2023\u2014Common ground and common sense","volume":"13","author":"Senapati","year":"2023","journal-title":"Blood Cancer J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2112","DOI":"10.1002\/cncr.23427","article-title":"Molecular monitoring in chronic myeloid leukemia","volume":"112","author":"Jabbour","year":"2008","journal-title":"Cancer"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2742","DOI":"10.1182\/blood-2016-01-690230","article-title":"Risk stratification of chromosomal abnormalities in chronic myelogenous leukemia in the era of tyrosine kinase inhibitor therapy","volume":"127","author":"Wang","year":"2016","journal-title":"Blood"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1038\/s41408-022-00696-4","article-title":"Chronic phase CML with sole P190 (e1a2) BCR::ABL1: Long-term outcome among ten consecutive cases","volume":"12","author":"Abdelmagid","year":"2022","journal-title":"Blood Cancer J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1182\/blood-2009-02-204693","article-title":"Chronic myeloid leukemia (CML) with P190BCR-ABL: Analysis of characteristics, outcomes, and prognostic significance","volume":"114","author":"Verma","year":"2009","journal-title":"Blood"},{"key":"ref_13","first-page":"2358","article-title":"Differential diagnosis of chronic myeloid leukemia and the related disorders","volume":"59","author":"Ashida","year":"2001","journal-title":"Nihon Rinsho. Jpn. J. Clin. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4271","DOI":"10.1038\/s41388-021-01861-y","article-title":"How artificial intelligence might disrupt diagnostics in hematology in the near future","volume":"40","author":"Walter","year":"2021","journal-title":"Oncogene"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1053\/j.semdp.2023.02.001","article-title":"Digital pathology and artificial intelligence as the next chapter in diagnostic hematopathology","volume":"40","author":"Lin","year":"2023","journal-title":"Semin. Diagn. Pathol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1038\/s41571-019-0252-y","article-title":"Artificial intelligence in digital pathology\u2014New tools for diagnosis and precision oncology","volume":"16","author":"Bera","year":"2019","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1046\/j.1365-2257.2003.00516.x","article-title":"Differential Counting of Blood Leukocytes Using Automated Microscopy and a Decision Support System Based on Artificial Neural Networks\u2014Evaluation of DiffMasterTM Octavia","volume":"25","author":"Swolin","year":"2003","journal-title":"Clin. Lab. Haematol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1182\/blood-2021-152447","article-title":"Automated peripheral blood cell differentiation using artificial intelligence-a study with more than 10,000 routine samples in a specialized leukemia laboratory","volume":"138","author":"Haferlach","year":"2021","journal-title":"Blood"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ahmed, N., Yigit, A., Isik, Z., and Alpkocak, A. (2019). Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network. Diagnostics, 9.","DOI":"10.3390\/diagnostics9030104"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e23154","DOI":"10.1097\/MD.0000000000023154","article-title":"AML, ALL, and CML Classification and Diagnosis Based on Bone Marrow Cell Morphology Combined with Convolutional Neural Network: A STARD Compliant Diagnosis Research","volume":"99","author":"Huang","year":"2020","journal-title":"Medicine"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"154517","DOI":"10.1016\/j.prp.2023.154517","article-title":"Association of pre-treatment bone marrow morphology and achievement of BCR-ABL1 transcript milestones in CML","volume":"246","author":"Grohovac","year":"2023","journal-title":"Pathol. Res. Pract."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1016\/j.ajpath.2022.03.016","article-title":"The Diagnosis of Chronic Myeloid Leukemia with Deep Adversarial Learning","volume":"192","author":"Zhang","year":"2022","journal-title":"Am. J. Pathol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e903","DOI":"10.1016\/j.clml.2021.06.025","article-title":"Accurate Machine-Learning-Based Classification of Leukemia from Blood Smear Images","volume":"21","author":"Dese","year":"2021","journal-title":"Clin. Lymphoma Myeloma Leuk."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"S14","DOI":"10.1002\/cyto.b.20363","article-title":"2006 Bethesda International Consensus recommendations on the immunophenotypic analysis of hematolymphoid neoplasia by flow cytometry: Optimal reagents and reporting for the flow cytometric diagnosis of hematopoietic neoplasia","volume":"72","author":"Wood","year":"2007","journal-title":"Cytom. B Clin. Cytom."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1002\/cyto.b.21764","article-title":"Flow Cytometry Assessment of CD26(+) Leukemic Stem Cells in Peripheral Blood: A Simple and Rapid New Diagnostic Tool for Chronic Myeloid Leukemia","volume":"96","author":"Raspadori","year":"2019","journal-title":"Cytom. B Clin. Cytom."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e14117","DOI":"10.1200\/JCO.2020.38.15_suppl.e14117","article-title":"Use of artificial intelligence to improve access to initial leukemia diagnosis in low- and middle-income countries","volume":"38","author":"Cerrato","year":"2020","journal-title":"J. Clin. Oncol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1016\/j.compbiomed.2013.06.004","article-title":"Discrimination of malignant neutrophils of chronic myelogenous leukemia from normal neutrophils by support vector machine","volume":"43","author":"Ni","year":"2013","journal-title":"Comput. Biol. Med."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, X., Zheng, B., Li, S., Mulvihill, J.J., Chen, X., and Liu, H. (2010). Automated identification of abnormal metaphase chromosome cells for the detection of chronic myeloid leukemia using microscopic images. J. Biomed. Opt., 15.","DOI":"10.1117\/1.3476336"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ram, M., Afrash, M.R., Moulaei, K., Parvin, M., Esmaeeli, E., Karbasi, Z., Heydari, S., and Sabahi, A. (2024). Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: A scoping review. BMC Cancer, 24.","DOI":"10.1186\/s12885-024-12764-y"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Shanbehzadeh, M., Afrash, M.R., Mirani, N., and Kazemi-Arpanahi, H. (2022). Comparing Machine Learning Algorithms to Predict 5-Year Survival in Patients with Chronic Myeloid Leukemia. BMC Med. Inform. Decis. Mak., 22.","DOI":"10.1186\/s12911-022-01980-w"},{"key":"ref_31","first-page":"335","article-title":"Application of an Artificial Neural Network in the Prognosis of Chronic Myeloid Leukemia","volume":"33","author":"Dey","year":"2011","journal-title":"Anal. Quant. Cytol. Histol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Banjar, H., Ranasinghe, D., Brown, F., Adelson, D., Kroger, T., Leclercq, T., White, D., Hughes, T., and Chaudhri, N. (2017). Modelling Predictors of Molecular Response to Frontline Imatinib for Patients with Chronic Myeloid Leukaemia. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0168947"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1002\/ajh.26047","article-title":"The LEukemia Artificial Intelligence Program (LEAP) in Chronic Myeloid Leukemia in Chronic Phase: A Model to Improve Patient Outcomes","volume":"96","author":"Sasaki","year":"2021","journal-title":"Am. J. Hematol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1080\/15384101.2017.1417706","article-title":"A Method of Gene Expression Data Transfer from Cell Lines to Cancer Patients for Machine-Learning Prediction of Drug Efficiency","volume":"17","author":"Borisov","year":"2018","journal-title":"Cell Cycle"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2443","DOI":"10.1038\/s41375-022-01680-4","article-title":"Identification of Key MicroRNAs as Predictive Biomarkers of Nilotinib Response in Chronic Myeloid Leukemia: A Sub-Analysis of the ENESTxtnd Clinical Trial","volume":"36","author":"Yen","year":"2022","journal-title":"Leukemia"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Alves, R., Gon\u00e7alves, A.C., Rutella, S., Almeida, A.M., De Las Rivas, J., Trougakos, I.P., and Sarmento Ribeiro, A.B. (2021). Resistance to Tyrosine Kinase Inhibitors in Chronic Myeloid Leukemia-From Molecular Mechanisms to Clinical Relevance. Cancers, 13.","DOI":"10.3390\/cancers13194820"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Portelli, S., Pat, M., Rodrigues, C.H.M., Nguyen, T.B., Pires, D.E.V., and Ascher, D.B. (2021). Structure-Guided Machine Learning Prediction of Drug Resistance Mutations in Abelson 1 Kinase. Comput. Struct. Biotechnol. J., 19.","DOI":"10.1016\/j.csbj.2021.09.016"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2872","DOI":"10.2174\/0929867330666230519105900","article-title":"Screening and Activity Evaluation of Novel BCR-ABL\/T315I Tyrosine Kinase Inhibitors","volume":"31","author":"Su","year":"2024","journal-title":"Curr. Med. Chem."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Mehra, N., Varmeziar, A., Chen, X., Kronick, O., Fisher, R., Kota, V., and Mitchell, C.S. (2022). Cross-Domain Text Mining to Predict Adverse Events from Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia. Cancers, 14.","DOI":"10.3390\/cancers14194686"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/s13045-021-01055-9","article-title":"Third-line therapy for chronic myeloid leukemia: Current status and future directions","volume":"14","author":"Cortes","year":"2021","journal-title":"J. Hematol. Oncol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1002\/cpt.2699","article-title":"Exposure\u2013Efficacy Analysis of Asciminib in Philadelphia Chromosome\u2013Positive Chronic Myeloid Leukemia in Chronic Phase","volume":"112","author":"Combes","year":"2022","journal-title":"Clin. Pharmacol. Ther."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Naveed, M., Ain, N.U., Aziz, T., Javed, K., Shabbir, M.A., Alharbi, M., Alsahammari, A., and Alasmari, A.F. (2023). Artificial Intelligence Assisted Pharmacophore Design for Philadelphia Chromosome-Positive Leukemia with Gamma-Tocotrienol: A Toxicity Comparison Approach with Asciminib. Biomedicines, 11.","DOI":"10.3390\/biomedicines11041041"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1517\/17460441.2011.588696","article-title":"Strategies of multi-objective optimization in drug discovery and development","volume":"6","author":"Nicolotti","year":"2011","journal-title":"Expert Opin. Drug Discov."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.drudis.2019.11.005","article-title":"Human ether-\u00e0-go-go-related potassium channel: Exploring SAR to improve drug design","volume":"25","author":"Cavalluzzi","year":"2020","journal-title":"Drug Discov. Today"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1080\/17460441.2017.1365056","article-title":"Ligand efficiency metrics in drug discovery: The pros and cons from a practical perspective","volume":"12","author":"Cavalluzzi","year":"2017","journal-title":"Expert Opin. Drug Discov."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5069","DOI":"10.1021\/jm020919o","article-title":"Multiobjective optimization in quantitative structure-activity relationships: Deriving accurate and interpretable QSARs","volume":"45","author":"Nicolotti","year":"2002","journal-title":"J. Med. Chem."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1021\/acs.jcim.8b00698","article-title":"A New Approach for Drug Target and Bioactivity Prediction: The Multifingerprint Similarity Search Algorithm (MuSSeL)","volume":"59","author":"Alberga","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ruff, K.M., and Pappu, R.V. (2021). AlphaFold and implications for intrinsically disordered proteins. J. Mol. Biol., 433.","DOI":"10.1016\/j.jmb.2021.167208"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3036","DOI":"10.1093\/bioinformatics\/btx350","article-title":"DeepSite: Protein-binding site predictor using 3Dconvolutional neural networks","volume":"33","author":"Doerr","year":"2017","journal-title":"Bioinformatics"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"123","DOI":"10.2174\/1573409916666191226110029","article-title":"Petra\/Osiris\/Molinspiration and Molecular Docking Analyses of 3-Hydroxy-Indolin-2-one Derivatives as Potential Antiviral Agents. Curr","volume":"17","author":"Rastija","year":"2021","journal-title":"Comput. Aided-Drug Des."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2358","DOI":"10.1021\/acs.jcim.7b00014","article-title":"Improved Method for the Identification and Validation of Allosteric Sites","volume":"57","author":"Song","year":"2017","journal-title":"J. Chem. Inf. Model."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"W374","DOI":"10.1093\/nar\/gky380","article-title":"CavityPlus: A web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction","volume":"46","author":"Xu","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"6512","DOI":"10.1021\/acs.jmedchem.9b00089","article-title":"Kinase Atlas: Druggability Analysis of Potential Allosteric Sites in Kinases","volume":"62","author":"Yueh","year":"2019","journal-title":"J. Med. Chem."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1038\/nprot.2015.043","article-title":"The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins","volume":"10","author":"Kozakov","year":"2015","journal-title":"Nat. Protoc."},{"key":"ref_55","first-page":"8738","article-title":"Machine Learning Models for Accurate Prediction of Kinase Inhibitors with Different Binding Modes","volume":"63","author":"Bajorath","year":"2019","journal-title":"J. Med. Chem."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4582","DOI":"10.1021\/acs.jcim.0c00517","article-title":"De novo drug design of targeted chemical libraries based on artificial intelligence and pair based multiobjective optimization","volume":"60","author":"Alberga","year":"2020","journal-title":"J. Chem. Inf. Model."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.1021\/acs.jcim.1c01554","article-title":"Discovery of Anticancer Hybrid Molecules by Supervised Machine Learning Models and in Vitro Validation in Drug Resistant Chronic Myeloid Leukemia Cells","volume":"62","author":"Melge","year":"2022","journal-title":"J. Chem. Inf. Model."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"D399","DOI":"10.1093\/nar\/gku928","article-title":"sc-PDB: A 3D-database of ligandable binding sites\u201410 years on","volume":"43","author":"Desaphy","year":"2015","journal-title":"Nucleic Acids Res."},{"key":"ref_59","unstructured":"(2020). Schr\u00f6dinger Release 2020-4: Desmond Molecular Dynamics System, D.E, Shaw Research\/Schr\u00f6dinger. Maestro-Desmond Interoperability Tools."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Malik, V., Radhakrishnan, N., Kaul, S.C., Wadhwa, R., and Sundar, D. (2022). Computational Identification of BCR-ABL Oncogenic Signaling as a Candidate Target of Withaferin A and Withanone. Biomolecules, 12.","DOI":"10.3390\/biom12020212"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1038\/nrd791","article-title":"Combinatorial informatics in the post-genomics era","volume":"1","author":"Agrafiotis","year":"2002","journal-title":"Nature Rev. Drug Discov."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1038\/425027a","article-title":"Join the crowd","volume":"425","author":"Ellis","year":"2003","journal-title":"Nature"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/S1570-9639(03)00167-5","article-title":"Macromolecular crowding: Qualitative and semiquantitative successes, quantitative challenges","volume":"1649","author":"Hall","year":"2003","journal-title":"Biochim. Biophys. Acta."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1038\/nature03192","article-title":"Chemical space and biology","volume":"16","author":"Dobson","year":"2004","journal-title":"Nature"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1021\/acs.jcim.5b00398","article-title":"Stargate GTM: Bridging Descriptor and Activity Spaces","volume":"55","author":"Gaspar","year":"2015","journal-title":"J. Chem. Inf. Model."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1007\/s10822-014-9760-0","article-title":"Chemical space networks: A powerful new paradigm for the description of chemical space","volume":"28","author":"Maggiora","year":"2014","journal-title":"J. Comput. Aided Mol. Des."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1007\/s10822-016-9906-3","article-title":"Lessons learned from the design of chemical space networks and opportunities for new applications","volume":"30","author":"Vogt","year":"2016","journal-title":"J. Comput. Aided Mol. Des."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"e202400050","DOI":"10.1002\/minf.202400050","article-title":"Navigating pharmacophore space to identify activity discontinuities: A case study with BCR-ABL","volume":"43","author":"Lejmi","year":"2024","journal-title":"Mol. Inform."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"3551","DOI":"10.1021\/acs.jmedchem.7b01890","article-title":"The Pharmacophore Network: A Computational Method for Exploring Structure-Activity Relationships from a Large Chemical Data Set","volume":"61","author":"Cuissart","year":"2018","journal-title":"J. Med. Chem."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1002\/art.38360","article-title":"Evidence from Raman spectroscopy of a putative link between inherent bone matrix chemistry and degenerative joint disease","volume":"66","author":"Kerns","year":"2014","journal-title":"Arthritis Rheumatol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"26625","DOI":"10.1074\/jbc.274.38.26625","article-title":"Raman spectroscopy, the sleeping giant in structural biology, awakes","volume":"274","author":"Carey","year":"1999","journal-title":"J. Biol. Chem."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Bonsignore, M., Trusso, S., De Pasquale, C., Ferlazzo, G., Allegra, A., Innao, V., Musolino, C., Franco, D., De Plano, M.L., and Guglielmino, S.P.P. (2021). A multivariate analysis of Multiple Myeloma subtype plasma cells. Spectrochim. Acta A Mol. Biomol. Spectrosc., 258.","DOI":"10.1016\/j.saa.2021.119813"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.saa.2017.06.020","article-title":"Raman spectroscopy differentiates between sensitive and resistant multiple myeloma cell lines","volume":"187","author":"Franco","year":"2017","journal-title":"Spectrochim. Acta A Mol Biomol. Spectrosc."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.saa.2016.01.032","article-title":"A micro-Raman spectroscopic investigation of leukemic U-937 cells in aged cultures","volume":"159","author":"Fazio","year":"2016","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"4756","DOI":"10.1002\/anie.201205748","article-title":"Surface-Enhanced Raman Spectroscopy: Concepts and Chemical Applications","volume":"53","year":"2014","journal-title":"Angew. Chem. Int. Ed."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.bios.2015.05.073","article-title":"Phage-AgNPs complex as SERS probe for U937 cell identification","volume":"74","author":"Lentini","year":"2015","journal-title":"Biosens. Bioelectron."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"127148","DOI":"10.1016\/j.talanta.2024.127148","article-title":"Combining array-assisted SERS microfluidic chips and machine learning algorithms for clinical leukemia phenotyping","volume":"283","author":"Yang","year":"2025","journal-title":"Talanta"},{"key":"ref_78","unstructured":"Carroll, J.J., Reynolds, D., Dickinson, I., Seaborne, A., Dollin, C., and Wilkinson, K. (2004, January 19\u201321). Jena: Implementing the Semantic Web Recommendations. Proceedings of the 13th International World Wide Web Conference on Alternate Track, Papers and Posters, New York, NY, USA."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Kozlenkov, A., and Schroeder, M. (2004, January 25\u201326). PROVA: Rule-Based Java-Scripting for a Bioinformatics Semantic Web. Proceedings of the Data Integration in the Life Sciences: First International Workshop, DILS 2004, Leipzig, Germany.","DOI":"10.1007\/978-3-540-24745-6_2"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Yang, G., Kifer, M., and Zhao, C.F. (2003). Lora-2: A Rule-Based Knowledge Representation and Inference Infrastructure for the Semantic Web. OTM Confederated International Conferences: On the Move to Meaningful Internet Systems, Springer.","DOI":"10.1007\/978-3-540-39964-3_43"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Allegra, A., Tonacci, A., Sciaccotta, R., Genovese, S., Musolino, C., Pioggia, G., and Gangemi, S. (2022). Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection. Cancers, 14.","DOI":"10.3390\/cancers14030606"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"103496","DOI":"10.1016\/j.autrev.2023.103496","article-title":"Machine learning application in autoimmune diseases: State of art and future prospectives","volume":"23","author":"Danieli","year":"2024","journal-title":"Autoimmun. Rev."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1016\/j.jpainsymman.2016.12.330","article-title":"Prognostic Tools in Patients with Advanced Cancer: A Systematic Review","volume":"53","author":"Simmons","year":"2017","journal-title":"J. Pain Symptom Manag."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Koteluk, O., Wartecki, A., Mazurek, S., Ko\u0142odziejczak, I., and Mackiewicz, A. (2021). How do machines learn? Artificial intelligence as a new era in medicine. J. Pers. Med., 11.","DOI":"10.3390\/jpm11010032"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"102009","DOI":"10.1016\/j.suronc.2023.102009","article-title":"New perspectives on cancer clinical research in the era of big data and machine learning","volume":"52","author":"Li","year":"2024","journal-title":"Surg. Oncol."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"497","DOI":"10.5582\/bst.2024.01342","article-title":"Applications of and issues with machine learning in medicine: Bridging the gap with explainable AI","volume":"18","author":"Karako","year":"2025","journal-title":"Biosci. Trends"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Bernardi, S., Vallati, M., and Gatta, R. (2024). Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?. Cancers, 16.","DOI":"10.3390\/cancers16050848"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1080\/14737140.2020.1829483","article-title":"On the road to treatment-free remission in chronic myeloid leukemia: What about \u2019the others\u2019?","volume":"20","author":"Stagno","year":"2020","journal-title":"Expert Rev. Anticancer Ther."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Kockerols, C., Valk, P.J.M., Hogenbirk, P., Geelen, I., Blijlevens, N.M.A., Janssen, J.J.W.M., Hoogendoorn, M., Kersting, S., Klein, S.K., and Daenen, L.G.M. (2025). Treatment-Free Remission Outcomes in a BCR::ABL1 Digital PCR Selected Clinical Cohort of CML Patients. Eur. J. Haematol.","DOI":"10.1111\/ejh.14368"}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/6\/2535\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:51:08Z","timestamp":1760028668000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/6\/2535"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,12]]},"references-count":89,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["ijms26062535"],"URL":"https:\/\/doi.org\/10.3390\/ijms26062535","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,12]]}}}