Link, K. E., Schnurman, Z., Liu, C., Kwon, Y. J., Jiang, L. Y., Nasir-Moin, M., Neifert, S., Alzate, J. D., Bernstein, K., Qu, T., Chen, V., Yang, E., Golfinos, J. G., Orringer, D., Kondziolka, D., & Oermann, E. K. (2024). Longitudinal deep neural networks for assessing metastatic brain cancer on a large open benchmark. Nature Communications, 15(1). https://doi.org/10.1038/s41467-024-52414-2

Choo, T.-H., Wall, M., Brodsky, B. S., Herzog, S., Mann, J. J., Stanley, B., & Galfalvy, H. (2024). Temporal prediction of suicidal ideation in an ecological momentary assessment study with recurrent neural networks. Journal of Affective Disorders, 360, 268–275. https://doi.org/10.1016/j.jad.2024.05.093

Kay, K., Biderman, N., Khajeh, R., Beiran, M., Cueva, C. J., Shohamy, D., Jensen, G., Wei, X.-X., Ferrera, V. P., & Abbott, L. (2024). Emergent neural dynamics and geometry for generalization in a transitive inference task. PLOS Computational Biology, 20(4), e1011954. https://doi.org/10.1371/journal.pcbi.1011954

Sasse, A., Ng, B., Spiro, A. E., Tasaki, S., Bennett, D. A., Gaiteri, C., De Jager, P. L., Chikina, M., & Mostafavi, S. (2023). Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings. Nature Genetics, 55(12), 2060–2064. https://doi.org/10.1038/s41588-023-01524-6

DePasquale, B., Sussillo, D., Abbott, L. F., & Churchland, M. M. (2023). The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks. Neuron, 111(5), 631-649.e10. https://doi.org/10.1016/j.neuron.2022.12.007

Thakoor, K. A., Yao, J., Bordbar, D., Moussa, O., Lin, W., Sajda, P., & Chen, R. W. S. (2022). A multimodal deep learning system to distinguish late stages of AMD and to compare expert vs. AI ocular biomarkers. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-06273-w

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