Machine Learning Approaches for Stem Cells

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.

CAS  PubMed  Google Scholar 

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.

Google Scholar 

Shortliffe EH, Sepúlveda MJ. Clinical decision support in the era of artificial intelligence. JAMA. 2018;320(21):2199–200.

Article  PubMed  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  PubMed  Google Scholar 

Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;21(19):221–48.

Article  Google Scholar 

Ker J, Wang L, Rao J, Lim T. Deep learning applications in medical image analysis. IEEE Access. 2017;29(6):9375–89.

Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Google Scholar 

Duddu S, Chakrabarti R, Ghosh A, Shukla PC. Hematopoietic stem cell transcription factors in cardiovascular pathology. Front Genet. 2020;16(11): 588602.

Article  Google Scholar 

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.

Article  Google Scholar 

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)

No login
gif