Sarker IH. Machine learning: algorithms, real-world applications and research directions. SN computer science. 2021;2(3):160.
Article PubMed PubMed Central Google Scholar
LeCun Y, Bengio Y, Hinton G. Deep learning nature. 2015;521(7553):436–44.
Silver D, Huang A, Maddison CJ, Guez A, Sifre L, Van Den Driessche G, et al. Mastering the game of Go with deep neural networks and tree search. Nature. 2016;529(7587):484–9.
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. Adv Neural Info Process Sys. 2017;30.
Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, et al. Mastering the game of Go without human knowledge. Nature. 2017;550(7676):354–9.
• Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022;28(1):31–8. The study discusses the potential impact of artificial intelligence (AI) on medicine and healthcare. It covers the key findings from a 2-year effort to track and share developments in medical AI, including advances in medical image analysis, potential uses of non-image data sources and unconventional problem formulations, and human–AI collaboration.
Article CAS PubMed Google Scholar
Subbiah V. The next generation of evidence-based medicine. Nat Med. 2023;16:1.
Shortliffe EH, Sepúlveda MJ. Clinical decision support in the era of artificial intelligence. JAMA. 2018;320(21):2199–200.
Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of artificial intelligence for computer-assisted drug discovery. Chem Rev. 2019;119(18):10520–94.
Article CAS PubMed Google Scholar
Meskó B, Drobni Z, Bényei É, Gergely B, Győrffy Z. Digital health is a cultural transformation of traditional healthcare. Mhealth. 2017;3.
Briganti G, Le Moine O. Artificial intelligence in medicine: today and tomorrow. Front Med. 2020;5(7):27.
He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25:30–6. https://doi.org/10.1038/s41591-018-0307-0.
Article CAS PubMed PubMed Central Google Scholar
Joshi G, Jain A, Adhikari S, Garg H, Bhandari M. FDA approved artificial intelligence and machine learning (AI/ML)-enabled medical devices: an updated 2022 landscape. medRxiv. 2022;2022–12.
Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ digital medicine. 2020;3(1):118.
Article PubMed PubMed Central Google Scholar
Grand View Research Homepage. Stem cells market size, share & trends analysis report by product (adult stem cells, human embryonic stem cells), by application, by technology, by therapy, by end use, by region, and segment forecasts, 2022–2030 [Internet]. Grand View Research; 2023 [cited 2022 Nov 15]. Available from: https://www.grandviewresearch.com/industry-analysis/stem-cells-market.
• Zaman WS, Karman SB, Ramlan EI, Tukimin SN, Ahmad MY. Machine learning in stem cells research: application for biosafety and bioefficacy assessment. IEEE Access. 2021;2(9):25926–45. This study focuses on the potential for machine learning–based analysis in assessing the biosafety and bio-efficacy of stem cells for clinical application, particularly in addressing the major concern of tumorigenicity.
TechCrunch Disrupt 2021. Cellino is using AI and machine learning to scale production of stem cell therapies [Internet]. TechCrunch; 2021 [cited 2022 Nov 25]. Available from: https://techcrunch.com/2021/09/22/cellino-is-using-ai-and-machine-learning-to-scale-production-of-stem-cell-therapies/
Libby AR, Briers D, Haghighi I, Joy DA, Conklin BR, Belta C, et al. Automated design of pluripotent stem cell self-organization. Cell Syst. 2019;9(5):483–95.
Article CAS PubMed PubMed Central Google Scholar
Wan Kamarul Zaman WS, Nurul AA, Nordin F. Stem cells and cancer stem cells: the Jekyll and Hyde Scenario and their implications in stem cell therapy. Biomedicines. 2021;9(9):1245.
• Ouyang JF, Chothani S, Rackham OJ. Deep learning models will shape the future of stem cell research. Stem Cell Reports. 2023;18(1):6–12. The study reviews the current state of deep-learning implementation in stem cell research and highlights future challenges for a successful adoption of the technology.
Article PubMed PubMed Central Google Scholar
Ren E, Kim S, Mohamad S, Huguet SF, Shi Y, Cohen AR, et al. Deep learning-enhanced morphological profiling predicts cell fate dynamics in real-time in hPSCs. bioRxiv. 2021;2021–07.
Guo J, Wang P, Sozen B, Qiu H, Zhu Y, Zhang X, et al. Machine learning-assisted high-content analysis of pluripotent stem cell-derived embryos in vitro. Stem cell reports. 2021;16(5):1331–46.
Article CAS PubMed PubMed Central Google Scholar
Stumpf PS, MacArthur BD. Machine learning of stem cell identities from single-cell expression data via regulatory network archetypes. Front Genet. 2019;22(10):2.
Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2018;19(6):1236–46.
Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;21(19):221–48.
Ker J, Wang L, Rao J, Lim T. Deep learning applications in medical image analysis. IEEE Access. 2017;29(6):9375–89.
Chen X, Wang X, Zhang K, Fung KM, Thai TC, Moore K, et al. Recent advances and clinical applications of deep learning in medical image analysis. Med Image Anal. 2022;4: 102444.
Buggenthin F, Buettner F, Hoppe PS, Endele M, Kroiss M, Strasser M, et al. Prospective identification of hematopoietic lineage choice by deep learning. Nat Methods. 2017;14(4):403–6.
Article CAS PubMed PubMed Central Google Scholar
Su YT, Lu Y, Chen M, Liu AA. Spatiotemporal joint mitosis detection using CNN-LSTM network in time-lapse phase contrast microscopy images. IEEE Access. 2017;29(5):18033–41.
Kusumoto D, Lachmann M, Kunihiro T, Yuasa S, Kishino Y, Kimura M, et al. Automated deep learning-based system to identify endothelial cells derived from induced pluripotent stem cells. Stem cell reports. 2018;10(6):1687–95.
Article CAS PubMed PubMed Central Google Scholar
Waisman A, La Greca A, Möbbs AM, Scarafía MA, Velazque NL, Neiman G, et al. Deep learning neural networks highly predict very early onset of pluripotent stem cell differentiation. Stem cell reports. 2019;12(4):845–59.
Article PubMed PubMed Central Google Scholar
Kavitha MS, Kurita T, Park SY, Chien SI, Bae JS, Ahn BC. Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells. PLoS ONE. 2017;12(12): e0189974.
Article PubMed PubMed Central Google Scholar
Orita K, Sawada K, Matsumoto N, Ikegaya Y. Machine-learning-based quality control of contractility of cultured human-induced pluripotent stem-cell-derived cardiomyocytes. Biochem Biophys Res Commun. 2020;526(3):751–5.
Article CAS PubMed Google Scholar
Zhu Y, Huang R, Wu Z, Song S, Cheng L, Zhu R. Deep learning-based predictive identification of neural stem cell differentiation. Nat Commun. 2021;12(1):2614.
Article CAS PubMed PubMed Central Google Scholar
Pan G, Jiang L, Tang J, Guo F. A novel computational method for detecting DNA methylation sites with DNA sequence information and physicochemical properties. Int J Mol Sci. 2018;19(2):511.
Article PubMed PubMed Central Google Scholar
Angermueller C, Lee HJ, Reik W, Stegle O. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol. 2017;18(1):1–3.
Zhang W, Spector TD, Deloukas P, Bell JT, Engelhardt BE. Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements. Genome Biol. 2015;16:1–20.
Nguyen QH, Lukowski SW, Chiu HS, Senabouth A, Bruxner TJ, Christ AN, et al. Single-cell RNA-seq of human induced pluripotent stem cells reveals cellular heterogeneity and cell state transitions between subpopulations. Genome Res. 2018;28(7):1053–66.
Article CAS PubMed PubMed Central Google Scholar
Sagar J, Chaib B, Sales K, Winslet M, Seifalian A. Role of stem cells in cancer therapy and cancer stem cells: a review. Cancer Cell Int. 2007;7(1):1–1.
Malta TM, Sokolov A, Gentles AJ, Burzykowski T, Poisson L, Weinstein JN, et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell. 2018;173(2):338–54.
Article CAS PubMed PubMed Central Google Scholar
Lian H, Han YP, Zhang YC, Zhao Y, Yan S, Li QF, et al. Integrative analysis of gene expression and DNA methylation through one-class logistic regression machine learning identifies stemness features in medulloblastoma. Mol Oncol. 2019;13(10):2227–45.
Article CAS PubMed PubMed Central Google Scholar
Pan S, Zhan Y, Chen X, Wu B, Liu B. Identification of biomarkers for controlling cancer stem cell characteristics in bladder cancer by network analysis of transcriptome data stemness indices. Front Oncol. 2019;4(9):613.
Chen W, Hong Z, Kang S, Lv X, Song C. Analysis of Stemness and Prognosis of Subtypes in Breast Cancer Using the Transcriptome Sequencing Data. Journal of Oncology. 2022;9:2022.
Duddu S, Chakrabarti R, Ghosh A, Shukla PC. Hematopoietic stem cell transcription factors in cardiovascular pathology. Front Genet. 2020;16(11): 588602.
Rauch A, Haakonsson AK, Madsen JG, Larsen M, Forss I, Madsen MR, et al. Osteogenesis depends on commissioning of a network of stem cell transcription factors that act as repressors of adipogenesis. Nat Genet. 2019;51(4):716–27.
Article CAS PubMed Google Scholar
Hamey FK, Göttgens B. Machine learning predicts putative hematopoietic stem cells within large single-cell transcriptomics data sets. Exp Hematol. 2019;1(78):11–20.
Fidanza A, Stumpf PS, Ramachandran P, Tamagno S, Babtie A, Lopez-Yrigoyen M, et al. Single-cell analyses and machine learning define hematopoietic progenitor and HSC-like cells derived from human PSCs. Blood. 2020;136(25):2893–904.
Comments (0)