Dreyfuss, M., Getz, B., Lebwohl, B., Ramni, O., Underberger, D., Ber, T. I., Steinberg-Koch, S., Jenudi, Y., Gazit, S., Patalon, T., Chodick, G., Shoenfeld, Y., & Ben-Tov, A. (2024). A machine learning tool for early identification of celiac disease autoimmunity. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-80817-0

Orouji, S., Liu, M. C., Korem, T., & Peters, M. A. K. (2024). Domain adaptation in small-scale and heterogeneous biological datasets. Science Advances, 10(51). https://doi.org/10.1126/sciadv.adp6040

Zolnoori, M., Zolnour, A., Vergez, S., Sridharan, S., Spens, I., Topaz, M., Noble, J. M., Bakken, S., Hirschberg, J., Bowles, K., Onorato, N., & McDonald, M. V. (2024). Beyond electronic health record data: leveraging natural language processing and machine learning to uncover cognitive insights from patient-nurse verbal communications. Journal of the American Medical Informatics Association. https://doi.org/10.1093/jamia/ocae300

Ishiguchi, H., Chen, Y., Huang, B., Gue, Y., Correa, E., Homma, S., Thompson, J. L. P., Qian, M., Lip, G. Y. H., & Abdul‐Rahim, A. H. (2024). Machine learning for stroke in heart failure with reduced ejection fraction but without atrial fibrillation: A post‐hoc analysis of the WARCEF trial. European Journal of Clinical Investigation. Portico. https://doi.org/10.1111/eci.14360

Xiao, H., Tian, J., Chen, Y., Wang, C., Zhang, Y., & Chen, L. (2024). Uncovering the features of industrial odors-derived environmental complaints and proactive countermeasures by using machine-learning. Journal of Environmental Management, 370, 122900. https://doi.org/10.1016/j.jenvman.2024.122900

Nemzow, L., Phillippi, M. A., Kanagaraj, K., Shuryak, I., Taveras, M., Wu, X., & Turner, H. C. (2024). Validation of a blood biomarker panel for machine learning-based radiation biodosimetry in juvenile and adult C57BL/6 mice. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-74953-w

Lang, F. M., Lee, B. C., Lotan, D., Sabuncu, M. R., & Topkara, V. K. (2024). Role of Artificial Intelligence and Machine Learning to Create Predictors, Enhance Molecular Understanding, and Implement Purposeful Programs for Myocardial Recovery. Methodist DeBakey Cardiovascular Journal, 20(4), 76–87. https://doi.org/10.14797/mdcvj.1392

Landau, A. Y., Blanchard, A., Kulkarni, P., Althobaiti, S., Idnay, B., Patton, D. U., Cato, K., & Topaz, M. (2024). Harnessing the Power of Machine Learning and Electronic Health Records to Support Child Abuse and Neglect Identification in Emergency Department Settings. Digital Health and Informatics Innovations for Sustainable Health Care Systems. https://doi.org/10.3233/shti240740

Wang, Z., Fang, Y., Liu, Z., Hao, N., Zhang, H. H., Sun, X., Que, J., & Ding, H. (2024). Adapting nanopore sequencing basecalling models for modification detection via incremental learning and anomaly detection. Nature Communications, 15(1). https://doi.org/10.1038/s41467-024-51639-5

Parrish, R. L., Buchman, A. S., Tasaki, S., Wang, Y., Avey, D., Xu, J., De Jager, P. L., Bennett, D. A., Epstein, M. P., & Yang, J. (2024). SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning. Nature Communications, 15(1). https://doi.org/10.1038/s41467-024-50983-w

Wang, E., Shuryak, I., & Brenner, D. J. (2024). A competing risks machine learning study of neutron dose, fractionation, age, and sex effects on mortality in 21,000 mice. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-68717-9

Taylor, B., Hobensack, M., Niño de Rivera, S., Zhao, Y., Masterson Creber, R., & Cato, K. (2024). Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review. JMIR Nursing, 7, e54810. https://doi.org/10.2196/54810

Li, Y., Li, J., Li, W., Liang, S., Wei, W., Chu, J., Lai, J., Lin, Y., Chen, H., Su, J., Hu, X., Wang, G., Meng, J., Jiang, J., Ye, L., & An, S. (2024). Scm6A: A Fast and Low-cost Method for Quantifying m6A Modifications at the Single-cell Level. Genomics, Proteomics & Bioinformatics. https://doi.org/10.1093/gpbjnl/qzae039

Prince, E. W., Apps, J. R., Jeang, J., Chee, K., Medlin, S., Jackson, E. M., Dudley, R., Limbrick, D., Naftel, R., Johnston, J., Feldstein, N., Prolo, L. M., Ginn, K., Niazi, T., Smith, A., Kilburn, L., Chern, J., Leonard, J., Lam, S., … Hankinson, T. C. (2024). Unraveling the complexity of the senescence-associated secretory phenotype in adamantinomatous craniopharyngioma using multimodal machine learning analysis. Neuro-Oncology, 26(6), 1109–1123. https://doi.org/10.1093/neuonc/noae015

Hrytsenko, Y., Shea, B., Elgart, M., Kurniansyah, N., Lyons, G., Morrison, A. C., Carson, A. P., Haring, B., Mitchell, B. D., Psaty, B. M., Jaeger, B. C., Gu, C. C., Kooperberg, C., Levy, D., Lloyd-Jones, D., Choi, E., Brody, J. A., Smith, J. A., Rotter, J. I., … Sofer, T. (2024). Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-62945-9

Liu, Y., Joly, R., Reading Turchioe, M., Benda, N., Hermann, A., Beecy, A., Pathak, J., & Zhang, Y. (2024). Preparing for the bedside—optimizing a postpartum depression risk prediction model for clinical implementation in a health system. Journal of the American Medical Informatics Association, 31(6), 1258–1267. https://doi.org/10.1093/jamia/ocae056

Emergency Department Risk Model: Timely Identification of Patients for Outpatient Care Coordination. (2024). The American Journal of Managed Care, 30(5), e147–e156. Portico. https://doi.org/10.37765/ajmc.2024.89542

Huang, Y., Rauh-Hain, J. A., McCoy, T. H., Hou, J. Y., Hillyer, G., Ferris, J. S., Hershman, D., Wright, J. D., & Melamed, A. (2024). Comparing survival of older ovarian cancer patients treated with neoadjuvant chemotherapy versus primary cytoreductive surgery: Reducing bias through machine learning. Gynecologic Oncology, 186, 9–16. https://doi.org/10.1016/j.ygyno.2024.03.016

Mohanty, S., Hassan, F. M., Lenke, L. G., Lewerenz, E., Passias, P. G., Klineberg, E. O., Lafage, V., Smith, J. S., Hamilton, D. K., Gum, J. L., Lafage, R., Mullin, J., Diebo, B., Buell, T. J., Kim, H. J., Kebaish, K., Eastlack, R., Daniels, A. H., Mundis, G., … Bess, S. (2024). Machine learning clustering of adult spinal deformity patients identifies four prognostic phenotypes: a multicenter prospective cohort analysis with single surgeon external validation. The Spine Journal, 24(6), 1095–1108. https://doi.org/10.1016/j.spinee.2024.02.010

Kim, B.-G., Kim, G., Abe, Y., Alonso, P., Ameis, S., Anticevic, A., Arnold, P. D., Balachander, S., Banaj, N., Bargalló, N., Batistuzzo, M. C., Benedetti, F., Bertolín, S., Beucke, J. C., Bollettini, I., Brem, S., Brennan, B. P., Buitelaar, J. K., … Calvo, R. (2024). White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group. Molecular Psychiatry, 29(4), 1063–1074. https://doi.org/10.1038/s41380-023-02392-6

Gue, Y., Correa, E., Thompson, J. L. P., Homma, S., Qian, M., & Lip, G. Y. H. (2023). Machine Learning Predicting Atrial Fibrillation as an Adverse Event in the Warfarin and Aspirin in Reduced Cardiac Ejection Fraction (WARCEF) Trial. The American Journal of Medicine, 136(11), 1099-1108.e2. https://doi.org/10.1016/j.amjmed.2023.07.019

Wu, Q., Schuemie, M. J., Suchard, M. A., Ryan, P., Hripcsak, G. M., Rohde, C. A., & Chen, Y. (2023). Padé approximant meets federated learning: A nearly lossless, one-shot algorithm for evidence synthesis in distributed research networks with rare outcomes. Journal of Biomedical Informatics, 145, 104476. https://doi.org/10.1016/j.jbi.2023.104476

Scheinost, D., Pollatou, A., Dufford, A. J., Jiang, R., Farruggia, M. C., Rosenblatt, M., Peterson, H., Rodriguez, R. X., Dadashkarimi, J., Liang, Q., Dai, W., Foster, M. L., Camp, C. C., Tejavibulya, L., Adkinson, B. D., Sun, H., Ye, J., Cheng, Q., Spann, M. N., … Westwater, M. L. (2023). Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer. Biological Psychiatry, 93(10), 893–904. https://doi.org/10.1016/j.biopsych.2022.10.014

Kebschull, M., Kroeger, A. T., & Papapanou, P. N. (2022). Differential Expression, Functional and Machine Learning Analysis of High-Throughput –Omics Data Using Open-Source Tools. Oral Biology, 317–351. https://doi.org/10.1007/978-1-0716-2780-8_19

Shuryak, I., Royba, E., Repin, M., Turner, H. C., Garty, G., Deoli, N., & Brenner, D. J. (2022). A machine learning method for improving the accuracy of radiation biodosimetry by combining data from the dicentric chromosomes and micronucleus assays. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-25453-2

Domínguez Conde, C., Xu, C., Jarvis, L. B., Rainbow, D. B., Wells, S. B., Gomes, T., Howlett, S. K., Suchanek, O., Polanski, K., King, H. W., Mamanova, L., Huang, N., Szabo, P. A., Richardson, L., Bolt, L., Fasouli, E. S., Mahbubani, K. T., Prete, M., Tuck, L., … Teichmann, S. A. (2022). Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science, 376(6594). https://doi.org/10.1126/science.abl5197

Gao, F., Zhang, W., Baccarelli, A. A., & Shen, Y. (2022). Predicting chemical ecotoxicity by learning latent space chemical representations. Environment International, 163, 107224. https://doi.org/10.1016/j.envint.2022.107224