<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">PAR</journal-id><journal-title-group><journal-title>Proceedings of Anticancer Research</journal-title></journal-title-group><issn>2208-3545</issn><eissn>2208-3553</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/par.v8i5.7082</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Exploring the Pivotal Association of AI in Cancer Stem Cells Detection and Treatment</title><url>https://artdesignp.com/journal/PAR/8/5/10.26689/par.v8i5.7082</url><author>AbubakarMuhammad</author><pub-date pub-type="publication-year"><year>2024</year></pub-date><volume>8</volume><issue>5</issue><history><date date-type="pub"><published-time>2024-09-25</published-time></date></history><abstract>Cancer stem cells (CSCs), or tumor-initiating cells (TICs), are cancerous cell subpopulations that remain while tumor cells propagate as a unique subset and exhibit multiple applications in several diseases. They are responsible for cancer cell initiation, development, metastasis, proliferation, and recurrence due to their self-renewal and differentiation abilities in many kinds of cells. Artificial intelligence (AI) has gained significant attention because of its vast applications in various fields including agriculture, healthcare, transportation, and robotics, particularly in detecting human diseases such as cancer. The division and metastasis of cancerous cells are not easy to identify at early stages due to their uncontrolled situations. It has provided some real-time pictures of cancer progression and relapse. The purpose of this review paper is to explore new investigations into the role of AI in cancer stem cell progression and metastasis and in regenerative medicines. It describes the association of machine learning and AI with CSCs along with its numerous applications from cancer diagnosis to therapy. This review has also provided key challenges and future directions of AI in cancer stem cell research diagnosis and therapeutic approach.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Ng DTK, Lee M, Tan RJY, et al., 2023, A Review of AI Teaching and Learning from 2000 to 2020. Educ Inf Technol, 28: 8445–8501. https://doi.org/10.1007/s10639-022-11491-w</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Nozari H, Ghahremani-Nahr J, Szmelter-Jarosz A, 2024, Chapter One – AI and Machine Learning for Real-World Problems. Advances in Computer, 134: 1–12. https://doi.org/10.1016/bs.adcom.2023.02.001</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B3" content-type="article"><label>3</label><element-citation publication-type="journal"><p>Karwasra R, Sharma S, Sharma I, et al., 2024, Autoimmune Autonomic Disorder: AI-Based Diagnosis and Prognosis, in Raza K, Singh S (eds) Artificial Intelligence and Autoimmune Diseases: Application in the Diagnosis, Prognosis, and Therapeutics. Springer Nature Singapore, Singapore, 77–98. https://doi.org/10.1007/978-981-99-9029-0_4</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B4" content-type="article"><label>4</label><element-citation publication-type="journal"><p>Saini A, Kumar M, Bhatt S, et al., 2020, Cancer Causes and Treatments. Int J Pharm Sci Res, 11: 3121–3134.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B5" content-type="article"><label>5</label><element-citation publication-type="journal"><p>Zhong L, Li Y, Xiong L, et al., 2021, Small Molecules in Targeted Cancer Therapy: Advances, Challenges, and Future Perspectives. Signal Transduct Target Ther, 6(1): 201. https://doi.org/10.1038/s41392-021-00572-w</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B6" content-type="article"><label>6</label><element-citation publication-type="journal"><p>Mani DR, Krug K, Zhang B, et al., 2022, Cancer Proteogenomics: Current Impact and Future Prospects. Nat Rev Cancer, 22(5): 298–313. https://doi.org/10.1038/s41568-022-00446-5</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B7" content-type="article"><label>7</label><element-citation publication-type="journal"><p>Cao R, Yuan L, Ma B, et al., 2020, Immune-Related Long Non-Coding RNA Signature Identified Prognosis and Immunotherapeutic Efficiency in Bladder Cancer (BLCA). Cancer Cell Int, 20: 276. https://doi.org/10.1186/s12935-020-01362-0</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B8" content-type="article"><label>8</label><element-citation publication-type="journal"><p>Xiao Y, Yu D, 2021, Tumor Microenvironment as A Therapeutic Target in Cancer. Pharmacol Ther, 221: 107753. https://doi.org/10.1016/j.pharmthera.2020.107753</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B9" content-type="article"><label>9</label><element-citation publication-type="journal"><p>DuCote TJ, Naughton KJ, Skaggs EM, et al., 2023, Using Artificial Intelligence to Identify Tumor Microenvironment Heterogeneity in Non-Small Cell Lung Cancers. Lab Invest, 103(8): 100176. https://doi.org/10.1016/j.labinv.2023.100176</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B10" content-type="article"><label>10</label><element-citation publication-type="journal"><p>Lee RY, Wu Y, Goh D, et al., 2023, Application of Artificial Intelligence to In Vitro Tumor Modeling and Characterization of the Tumor Microenvironment. Adv Healthc Mater, 12(14): e2202457. https://doi.org/10.1002/adhm.202202457</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B11" content-type="article"><label>11</label><element-citation publication-type="journal"><p>Noto JM, Piazuelo MB, Romero-Gallo J, et al., 2023, Targeting Hypoxia-Inducible Factor-1 Alpha Suppresses Helicobacter pylori-Induced Gastric Injury via Attenuation of Both Cag-Mediated Microbial Virulence and Proinflammatory Host Responses. Gut Microbes, 15(2): 2263936. https://doi.org/10.1080/19490976.2023.2263936</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B12" content-type="article"><label>12</label><element-citation publication-type="journal"><p>Sutherland TE, Dyer DP, Allen JE, 2023, The Extracellular Matrix and the Immune System: A Mutually Dependent Relationship. Science, 379(6633): eabp8964. https://doi.org/10.1126/science.abp8964</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B13" content-type="article"><label>13</label><element-citation publication-type="journal"><p>Zhang Y, Zhang Z, 2020, The History and Advances in Cancer Immunotherapy: Understanding the Characteristics of Tumor-Infiltrating Immune Cells and Their Therapeutic Implications. Cell Mol Immunol, 17(8): 807–821. https://doi.org/10.1038/s41423-020-0488-6</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B14" content-type="article"><label>14</label><element-citation publication-type="journal"><p>Downes N, Niskanen H, Tomas Bosch V, et al., 2023, Hypoxic Regulation of Hypoxia Inducible Factor 1 Alpha via Antisense Transcription. J Biol Chem, 299(11): 105291. https://doi.org/10.1016/j.jbc.2023.105291</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B15" content-type="article"><label>15</label><element-citation publication-type="journal"><p>Ribeiro Franco PI, Rodrigues AP, de Menezes LB, et al., 2020, Tumor Microenvironment Components: Allies of Cancer Progression. Pathol Res Pract, 216(1): 152729. https://doi.org/10.1016/j.prp.2019.152729</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B16" content-type="article"><label>16</label><element-citation publication-type="journal"><p>Moindjie H, Rodrigues-Ferreira S, Nahmias C, 2021, Mitochondrial Metabolism in Carcinogenesis and Cancer Therapy. Cancers (Basel), 13(13): 3311. https://doi.org/10.3390/cancers13133311</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B17" content-type="article"><label>17</label><element-citation publication-type="journal"><p>Nong S, Han X, Xiang Y, et al., 2023, Metabolic Reprogramming in Cancer: Mechanisms and Therapeutics. MedComm (2020), 4(2): e218. https://doi.org/10.1002/mco2.218</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B18" content-type="article"><label>18</label><element-citation publication-type="journal"><p>Khorasani A, Shahbazi-Gahrouei D, Safari A, 2023, Recent Metal Nanotheranostics for Cancer Diagnosis and Therapy: A Review. Diagnostics (Basel), 13(5): 833. https://doi.org/10.3390/diagnostics13050833</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B19" content-type="article"><label>19</label><element-citation publication-type="journal"><p>Awasthi R, Mishra S, Cywinski JB, et al., 2023, Quantitative and Qualitative Evaluation of the Recent Artificial Intelligence in Healthcare Publications using Deep-Learning. medRxiv, 2023. https://doi.org/10.1101/2022.12.31.22284092</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B20" content-type="article"><label>20</label><element-citation publication-type="journal"><p>Chugh V, Basu A, Kaushik A, et al., 2024, Employing Nano-Enabled Artificial Intelligence (AI)-Based Smart Technologies for Prediction, Screening, and Detection of Cancer. Nanoscale, 16(11): 5458–5486. https://doi.org/10.1039/d3nr05648a</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B21" content-type="article"><label>21</label><element-citation publication-type="journal"><p>Zhou R, Tong F, Zhang Y, et al., 2023, Genomic Alterations Associated with Pseudoprogression and Hyperprogressive Disease During Anti-PD1 Treatment for Advanced Non-Small-Cell Lung Cancer. Front Oncol, 13: 1231094. https://doi.org/10.3389/fonc.2023.1231094</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B22" content-type="article"><label>22</label><element-citation publication-type="journal"><p>Rasool S, Ali M, Shahroz HM, et al., 2024, Innovations in AI-Powered Healthcare: Transforming Cancer Treatment with Innovative Methods. BULLET: Jurnal Multidisiplin Ilmu, 3(1): 118–128.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B23" content-type="article"><label>23</label><element-citation publication-type="journal"><p>Choudhary A, Ahlawat S, Urooj S, et al., 2023, A Deep Learning-Based Framework for Retinal Disease Classification. Healthcare (Basel), 11(2): 212. https://doi.org/10.3390/healthcare11020212</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B24" content-type="article"><label>24</label><element-citation publication-type="journal"><p>Khan SU, Jan S, Fatima K, et al., 2024, Future Directions and Challenges in Overcoming Drug Resistance in Cancer, in Khan SU, Malik F (eds) Drug Resistance in Cancer: Mechanisms and Strategies. Springer Nature Singapore, Singapore, 351–372. https://doi.org/10.1007/978-981-97-1666-1_12</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B25" content-type="article"><label>25</label><element-citation publication-type="journal"><p>Sudha B, Suganya K, Swathi K, et al., 2022, Artificial Intelligence is Revolutionizing Cancer Research, in Devi KG, Balasubramanian K, Ngoc LA (eds) Machine Learning and Deep Learning Techniques for Medical Science. CRC Press, Boca Raton (FL), 263–278.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B26" content-type="article"><label>26</label><element-citation publication-type="journal"><p>Chen ZH, Lin L, Wu CF, et al., 2021, Artificial Intelligence for Assisting Cancer Diagnosis and Treatment in the Era of Precision Medicine. Cancer Commun (Lond), 41(11): 1100–1115. https://doi.org/10.1002/cac2.12215</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B27" content-type="article"><label>27</label><element-citation publication-type="journal"><p>Rao C, Liu Y, 2020, Three-Dimensional Convolutional Neural Network (3D-CNN) for Heterogeneous Material Homogenization. Comput Mater Sci, 184: 109850. https://doi.org/10.1016/j.commatsci.2020.109850</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B28" content-type="article"><label>28</label><element-citation publication-type="journal"><p>Luchini C, Pea A, Scarpa A, 2022, Artificial Intelligence in Oncology: Current Applications and Future Perspectives. Br J Cancer, 126(1): 4–9. https://doi.org/10.1038/s41416-021-01633-1</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B29" content-type="article"><label>29</label><element-citation publication-type="journal"><p>Wilhelm M, Zolg DP, Graber M, et al., 2021, Deep Learning Boosts Sensitivity of Mass Spectrometry-Based Immunopeptidomics. Nat Commun, 12(1): 3346. https://doi.org/10.1038/s41467-021-23713-9. Erratum in Nat Commun, 12(1): 4002. https://doi.org/10.1038/s41467-021-24263-w</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B30" content-type="article"><label>30</label><element-citation publication-type="journal"><p>Wu J, Hicks C, 2021, Breast Cancer Type Classification Using Machine Learning. J Pers Med, 11(2): 61. https://doi.org/10.3390/jpm11020061</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B31" content-type="article"><label>31</label><element-citation publication-type="journal"><p>Tran TO, Vo TH, Le NQK, 2024, Omics-Based Deep Learning Approaches for Lung Cancer Decision-Making and Therapeutics Development. Brief Funct Genomics, 23(3): 181–192. https://doi.org/10.1093/bfgp/elad031. Erratum in Brief Funct Genomics, elad046. https://doi.org/10.1093/bfgp/elad046</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B32" content-type="article"><label>32</label><element-citation publication-type="journal"><p>Sufyan M, Shokat Z, Ashfaq UA, 2023, Artificial Intelligence in Cancer Diagnosis and Therapy: Current Status and Future Perspective. Comput Biol Med, 165: 107356. https://doi.org/10.1016/j.compbiomed.2023.107356</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B33" content-type="article"><label>33</label><element-citation publication-type="journal"><p>Tan P, Chen X, Zhang H, et al., 2023, Artificial Intelligence Aids in Development of Nanomedicines for Cancer Management. Semin Cancer Biol, 89: 61–75. https://doi.org/10.1016/j.semcancer.2023.01.005</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B34" content-type="article"><label>34</label><element-citation publication-type="journal"><p>Wang C, Yu P, Zhang H, et al., 2023, Artificial Intelligence-Based Prediction of Cervical Lymph Node Metastasis in Papillary Thyroid Cancer with CT. Eur Radiol, 33(10): 6828–6840. https://doi.org/10.1007/s00330-023-09700-2</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B35" content-type="article"><label>35</label><element-citation publication-type="journal"><p>Tosca EM, Ronchi D, Facciolo D, et al., 2023, Replacement, Reduction, and Refinement of Animal Experiments in Anticancer Drug Development: The Contribution of 3D In Vitro Cancer Models in the Drug Efficacy Assessment. Biomedicines, 11(4): 1058. https://doi.org/10.3390/biomedicines11041058</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B36" content-type="article"><label>36</label><element-citation publication-type="journal"><p>Ho D, 2020, Artificial Intelligence in Cancer Therapy. Science, 367(6481): 982–983. https://doi.org/10.1126/science.aaz3023</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B37" content-type="article"><label>37</label><element-citation publication-type="journal"><p>Dandale MN, Yadav AP, Reddy PSK, et al., 2024, Deep Learning Enhanced Drug Discovery for Novel Biomaterials in Regenerative Medicine Utilizing Graph Neural Network Approach for Predicting Cellular Responses. The Scientific Temper, 15(1): 1588–1594. https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.1.04</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B38" content-type="article"><label>38</label><element-citation publication-type="journal"><p>Altyar AE, El-Sayed A, Abdeen A, et al., 2023, Future Regenerative Medicine Developments and Their Therapeutic Applications. Biomed Pharmacother, 158: 114131. https://doi.org/10.1016/j.biopha.2022.114131</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B39" content-type="article"><label>39</label><element-citation publication-type="journal"><p>Farajpour H, Banimohamad-Shotorbani B, Rafiei-Baharloo M, et al., 2024, Application of Artificial Intelligence in Regenerative Medicine. Neurosci J Shefaye Khatam, 11(4): 94–107. https://doi.org/10.61186/shefa.11.4.94</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B40" content-type="article"><label>40</label><element-citation publication-type="journal"><p>Hasselgren C, Oprea TI, 2024, Artificial Intelligence for Drug Discovery: Are We There Yet? Annu Rev Pharmacol Toxicol, 64: 527–550. https://doi.org/10.1146/annurev-pharmtox-040323-040828</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B41" content-type="article"><label>41</label><element-citation publication-type="journal"><p>Jeyaraman M, Ratna HVK, Jeyaraman N, et al., 2023, Leveraging Artificial Intelligence and Machine Learning in Regenerative Orthopedics: A Paradigm Shift in Patient Care. Cureus, 15(11): e49756. https://doi.org/10.7759/cureus.49756</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B42" content-type="article"><label>42</label><element-citation publication-type="journal"><p>Qureshi R, Irfan M, Gondal TM, et al., 2023, AI in Drug Discovery and Its Clinical Relevance. Heliyon, 9(7): e17575. https://doi.org/10.1016/j.heliyon.2023.e17575</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B43" content-type="article"><label>43</label><element-citation publication-type="journal"><p>Fan B 2023, Limitations and Ethical Implications of Artificial Intelligence, in Xia M, Jiang H (eds) Artificial Intelligence in Anesthesiology. Springer Nature Singapore, Singapore, 109–113. https://doi.org/10.1007/978-981-99-5925-9_12</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B44" content-type="article"><label>44</label><element-citation publication-type="journal"><p>Elemento O, Leslie C, Lundin J, et al., 2021, Artificial Intelligence in Cancer Research, Diagnosis and Therapy. Nat Rev Cancer, 21(12): 747–752. https://doi.org/10.1038/s41568-021-00399-1</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B45" content-type="article"><label>45</label><element-citation publication-type="journal"><p>Deus IA, Mano JF, Custodio CA, 2020, Perinatal Tissues and Cells in Tissue Engineering and Regenerative Medicine. Acta Biomater, 110: 1–14. https://doi.org/10.1016/j.actbio.2020.04.035</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B46" content-type="article"><label>46</label><element-citation publication-type="journal"><p>Alsuliman T, Humaidan D, Sliman L, 2020, Machine Learning and Artificial Intelligence in the Service of Medicine: Necessity or Potentiality? Curr Res Transl Med, 68(4): 245–251. https://doi.org/10.1016/j.retram.2020.01.002</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B47" content-type="article"><label>47</label><element-citation publication-type="journal"><p>Greenberg ZF, Graim KS, He M, 2023, Towards Artificial Intelligence-Enabled Extracellular Vesicle Precision Drug Delivery. Adv Drug Deliv Rev, 199: 114974. https://doi.org/10.1016/j.addr.2023.114974</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B48" content-type="article"><label>48</label><element-citation publication-type="journal"><p>Khan B, Fatima H, Qureshi A, et al., 2023, Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector. Biomed Mater Devices, 2023: 1–8. https://doi.org/10.1007/s44174-023-00063-2</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B49" content-type="article"><label>49</label><element-citation publication-type="journal"><p>Abubakar M, Bukhari SMA, Mustfa W, et al., 2024, Skin Cancer and Human Papillomavirus. J Popul Ther Clin Pharmacol, 31(2): 790–816.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B50" content-type="article"><label>50</label><element-citation publication-type="journal"><p>Chassagnon G, De Margerie-Mellon C, Vakalopoulou M, et al., 2023, Artificial Intelligence in Lung Cancer: Current Applications and Perspectives. Jpn J Radiol, 41(3): 235–244. https://doi.org/10.1007/s11604-022-01359-x</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B51" content-type="article"><label>51</label><element-citation publication-type="journal"><p>Santa-Rosario JC, Gustafson EA, Sanabria Bellassai DE, et al., 2024, Validation and Three Years of Clinical Experience in Using An Artificial Intelligence Algorithm as A Second Read System for Prostate Cancer Diagnosis-Real-World Experience. J Pathol Inform, 15: 100378. https://doi.org/10.1016/j.jpi.2024.100378</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B52" content-type="article"><label>52</label><element-citation publication-type="journal"><p>Damiani C, Kalliatakis G, Sreenivas M, et al., 2023, Evaluation of an AI Model to Assess Future Breast Cancer Risk. Radiology, 307(5): e222679. https://doi.org/10.1148/radiol.222679</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B53" content-type="article"><label>53</label><element-citation publication-type="journal"><p>Yin Z, Yao C, Zhang L, et al., 2023, Application of Artificial Intelligence in Diagnosis and Treatment of Colorectal Cancer: A Novel Prospect. Front Med (Lausanne), 10: 1128084. https://doi.org/10.3389/fmed.2023.1128084</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B54" content-type="article"><label>54</label><element-citation publication-type="journal"><p>Xu H, Tang RSY, Lam TYT, et al., 2023, Artificial Intelligence-Assisted Colonoscopy for Colorectal Cancer Screening: A Multicenter Randomized Controlled Trial. Clin Gastroenterol Hepatol, 21(2): 337–346.e3. https://doi.org/10.1016/j.cgh.2022.07.006</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B55" content-type="article"><label>55</label><element-citation publication-type="journal"><p>Jin P, Ji X, Kang W, et al., 2020, Artificial Intelligence in Gastric Cancer: A Systematic Review. J Cancer Res Clin Oncol, 146(9): 2339–2350. https://doi.org/10.1007/s00432-020-03304-9</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B56" content-type="article"><label>56</label><element-citation publication-type="journal"><p>Medina-Franco JL, 2021, Grand Challenges of Computer-Aided Drug Design: The Road Ahead. Front Drug Discov, 1: 728551. https://doi.org/10.3389/fddsv.2021.728551</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B57" content-type="article"><label>57</label><element-citation publication-type="journal"><p>Seo S, Lee JW, 2024, Applications of Big Data and AI-Driven Technologies in CADD (Computer-Aided Drug Design), in Gore M, Jagtap UB (Eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 2714. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3441-7_16</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B58" content-type="article"><label>58</label><element-citation publication-type="journal"><p>Niu T, Zhang W, Zhao R, 2024, Solution-Oriented Agent-Based Models Generation with Verifier-Assisted Iterative In-Context Learning. arXiv, Preprint. https://doi.org/10.48550/arXiv.2402.02388</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B59" content-type="article"><label>59</label><element-citation publication-type="journal"><p>Hunsberger J, Simon C, Zylberberg C, et al., 2020, Improving Patient Outcomes with Regenerative Medicine: How the Regenerative Medicine Manufacturing Society Plans to Move the Needle Forward in Cell Manufacturing, Standards, 3D Bioprinting, Artificial Intelligence-Enabled Automation, Education, and Training. Stem Cells Transl Med, 9(7): 728–733. https://doi.org/10.1002/sctm.19-0389</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B60" content-type="article"><label>60</label><element-citation publication-type="journal"><p>Lwakatare LE, Raj A, Crnkovic I, et al., 2020, Large-Scale Machine Learning Systems in Real-World Industrial Settings: A Review of Challenges and Solutions. Inf Softw Technol, 127: 106368. https://doi.org/10.1016/j.infsof.2020.106368</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B61" content-type="article"><label>61</label><element-citation publication-type="journal"><p>Stalidzans E, Zanin M, Tieri P, et al., 2020, Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice. Netw Syst Med, 3(1): 36–56. https://doi.org/10.1089/nsm.2020.0002</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B62" content-type="article"><label>62</label><element-citation publication-type="journal"><p>Srinivasan M, Thangaraj SR, Ramasubramanian K, et al., 2023, Chapter 10 – Artificial Intelligence in Stem Cell Therapies and Organ Regeneration, in Sharma CP, Chandy T, Thomas V (Eds) Artificial Intelligence in Tissue and Organ Regeneration. Academic Press, Cambridge (MA), 175–190. https://doi.org/10.1016/B978-0-443-18498-7.00001-6</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B63" content-type="article"><label>63</label><element-citation publication-type="journal"><p>Takahashi T, Donahue RP, Nordberg RC, et al., 2023, Commercialization of Regenerative-Medicine Therapies. Nat Rev Bioeng, 1(12): 906–929. https://doi.org/10.1038/s44222-023-00095-9</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B64" content-type="article"><label>64</label><element-citation publication-type="journal"><p>Panuccio G, Subramaniyam NP, Canal-Alonso A, et al., 2024, Chapter 13 – Using AI to Steer Brain Regeneration: The Enhanced Regenerative Medicine Paradigm, in Carpentieri B, Lecca P (Eds) Big Data Analysis and Artificial Intelligence for Medical Sciences. John Wiley &amp; Sons, Hoboken (NJ), 273–307. https://doi.org/10.1002/9781119846567.ch13</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B65" content-type="article"><label>65</label><element-citation publication-type="journal"><p>Rana M, Bhushan M, 2022, Machine Learning and Deep Learning Approach for Medical Image Analysis: Diagnosis to Detection. Multimed Tools Appl, 2022: 1–39. https://doi.org/10.1007/s11042-022-14305-w</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B66" content-type="article"><label>66</label><element-citation publication-type="journal"><p>Ngugi LC, Abelwahab M, Abo-Zahhad M, 2021, Recent Advances in Image Processing Techniques for Automated Leaf Pest and Disease Recognition – A Review. Inf Process Agric, 8(1): 27–51. https://doi.org/10.1016/j.inpa.2020.04.004</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B67" content-type="article"><label>67</label><element-citation publication-type="journal"><p>Dara S, Dhamercherla S, Jadav SS, et al., 2022, Machine Learning in Drug Discovery: A Review. Artif Intell Rev, 55(3): 1947–1999. https://doi.org/10.1007/s10462-021-10058-4</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B68" content-type="article"><label>68</label><element-citation publication-type="journal"><p>Udegbe FC, Ebulue OR, Ebulue CC, et al., 2024, Machine Learning in Drug Discovery: A Critical Review of Applications and Challenges. Computer Science &amp; IT Research Journal, 5(4): 892–902. https://doi.org/10.51594/csitrj.v5i4.1048</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B69" content-type="article"><label>69</label><element-citation publication-type="journal"><p>Liao Y, Wang Y, Cheng M, et al., 2020, Weighted Gene Coexpression Network Analysis of Features That Control Cancer Stem Cells Reveals Prognostic Biomarkers in Lung Adenocarcinoma. Front Genet, 11: 311. https://doi.org/10.3389/fgene.2020.00311</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B70" content-type="article"><label>70</label><element-citation publication-type="journal"><p>Li Z, Zhang H, Wang X, et al., 2022, Identification of Cuproptosis-Related Subtypes, Characterization of Tumor Microenvironment Infiltration, and Development of A Prognosis Model in Breast Cancer. Front Immunol, 13: 996836. https://doi.org/10.3389/fimmu.2022.996836</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
