Page 28 - JCTR-11-5
P. 28

Journal of Clinical and
            Translational Research                                                AI and LLMs in iPSC cardiac research



               doi: 10.1186/s40364-024-00672-z                    doi: 10.1002/qub2.65
            32.  Talman V, Teppo J, Pöhö P,  et al. Molecular atlas of   44.  Hochstadt A, Barbhaiya C, Aizer A, et al. Performance of
               postnatal  mouse  heart  development.  J  Am Heart Assoc.   a protein language model for variant annotation in cardiac
               2018;7(20):e010378.                                disease. J Am Heart Assoc. 2024;13(20):e036921.
               doi: 10.1161/jaha.118.010378                       doi: 10.1161/jaha.124.036921
            33.  Hayat  R.  Dynamics  of  metabolism  and  regulation  of   45.  Llm-Jp, Aizawa A, Aramaki E,  et al.  LLM-JP: A  Cross-
               epigenetics during cardiomyocytes maturation. Cell Biol Int.   Organizational Project for the Research and Development
               2022;47(1):30-40.                                  of  Fully  Open  Japanese  LLMs. New  York: arXiv Cornell
                                                                  University; 2024.
               doi: 10.1002/cbin.11931
                                                                  doi: 10.48550/arxiv.2407.03963
            34.  Huang L, Wang Q, Gu S, Cao N. Integrated metabolic and
               epigenetic mechanisms in cardiomyocyte proliferation.   46.  Cui H, Wang C, Maan H,  et al. scGPT: Toward building
               J Mol Cell Cardiol. 2023;181:79-88.                a foundation model for single-cell multi-omics using
                                                                  generative AI. Nat Methods. 2024;21(8):1470-1480.
               doi: 10.1016/j.yjmcc.2023.06.002
                                                                  doi: 10.1038/s41592-024-02201-0
            35.  Rommel C, Hein L. Four dimensions of the cardiac
               myocyte epigenome: From fetal to adult heart. Curr Cardiol   47.  Hao M, Gong J, Zeng X, et al. Large Scale Foundation Model
               Rep. 2020;22(5):26.                                on Single-Cell Transcriptomics. bioRxiv New  York: Cold
                                                                  Spring Harbor Laboratory; 2023.
               doi: 10.1007/s11886-020-01280-7
                                                                  doi: 10.1101/2023.05.29.542705
            36.  Zamora-Dorta M, Laine-Menéndez S, Abia D,  et  al.
               Time-resolved  mitochondrial  screen  identifies  48.  Li Y, Mamouei M, Khorshidi GS,  et al.  Hi-BEHRT:
               regulatory  components  of  oxidative  metabolism.  EMBO   Hierarchical Transformer-based Model for Accurate Prediction
               Rep. 2025;26:3045-3074.                            of Clinical Events Using Multimodal Longitudinal Electronic
                                                                  Health Records. New York: arXiv Cornell University; 2021.
               doi: 10.1038/s44319-025-00459-9
                                                                  doi: 10.48550/arxiv.2106.11360
            37.  Liu B, Chang H, Yang D, et al. A deep learning framework
               assisted  echocardiography  with  diagnosis,  lesion  49.  Ning Z, Jiang X, Huang H, et al. Machine learning integration
               localization, phenogrouping heterogeneous disease, and   of multimodal data identifies key features of circulating
               anomaly detection. Sci Rep. 2023;13:3.             NT-proBNP in people without cardiovascular diseases. Sci
                                                                  Rep. 2025;15(1):12015.
               doi: 10.1038/s41598-022-27211-w
                                                                  doi: 10.1038/s41598-025-96689-x
            38.  Panahiazar M, Taslimitehrani V, Pereira N, Pathak J. Using
               EHRs and machine learning for heart failure survival   50.  Neyazi M, Bremer JP, Knorr MS, et al. Deep learning-based
               analysis. Stud Health Technol Inform. 2015;216:40-44.  NT-proBNP prediction from the ECG for risk assessment in
                                                                  the community. Clin Chem Lab Med. 2023;62(4):740-752.
               doi: 10.3233/978-1-61499-564-7-40
                                                                  doi: 10.1515/cclm-2023-0743
            39.  Pan J, Lee S, Cheligeer C, et al. Integrating large language
               models with human expertise for disease detection in   51.  Gunčar G, Kukar M, Smole T, et al. Differentiating viral and
               electronic health records. Comput Biol Med. 2025;191:110161.  bacterial infections: A  machine learning model based on
                                                                  routine blood test values. Heliyon. 2024;10(8):e29372.
               doi: 10.1016/j.compbiomed.2025.110161
                                                                  doi: 10.1016/j.heliyon.2024.e29372
            40.  Zhu Y, Huang R, Wu Z, et al. Deep learning-based predictive
               identification  of  neural  stem  cell differentiation.  Nat   52.  Maxwell YL.  In  HFREF,  AI Shows Promise for Predicting
               Commun. 2021;12:2614.                              Beta Blocker Response; 2021. Available from: https://www.
                                                                  tctmd.com/news/hfref-ai-shows-promise-predicting-beta-
               doi: 10.1038/s41467-021-22758-0
                                                                  blocker-response [Last accessed on 2025 May 20].
            41.  Pickard J, Choi MA, Oliven N, et al. Bioinformatics Retrieval   53.  Kyro GW, Martin MT, Watt ED, Batista VS. CardioGenAI:
               Augmentation Data (BRAD) Digital Assistant.  New  York:   A  Machine Learning-Based Framework for Re-Engineering
               arXiv Cornell University; 2024.
                                                                  Drugs for Reduced HERG Liability. New York: arXiv Cornell
               doi: 10.48550/arxiv.2409.02864                     University; 2024.
            42.  Sapunov G. Deep Learning with JAX. United States: Simon      doi: 10.48550/arxiv.2403.07632
               and Schuster; 2024.
                                                               54.  Chiu CE, Pinto AL, Chowdhury RA, Christensen K,
            43.  Hao M, Wei L, Yang F, et al. Current opinions on large   Varela M. Characterisation of Anti-Arrhythmic Drug Effects
               cellular models. Quant Biol. 2024;12(4):433-443.   on Cardiac Electrophysiology using Physics-Informed Neural


            Volume 11 Issue 5 (2025)                        22                         doi: 10.36922/JCTR025230026
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