Pre-Prints


Pre-prints
  1. S. D. Curtis, T. Liu, Y. Bai, Y. Wang, S. Panda, A. Li, H. Xu, E. O’Reilly, L. Dobbyn, M. Popoli, J. Ptak, N. Silliman, C. Thoburn, J. Tie, P. Gibbs, L. T. Ho-Pham, B. N. Tran, T. S. Tran, T. V. Nguyen, M. F. Konig, M. Petri, A. Rosen, C. A. Mecoli, A. A. Shah, F. Mulder, N. van Es, PLATO-VTE Study Group, C. Bettegowda, K. W. Kinzler, N. Papadopoulos, J. T. Vogelstein, Bert Vogelstein, and C. Douville. Fragmentation signatures in cancer patients resemble those of patients with vascular or autoimmune diseases. PNAS, 2025.
  2. S. D. Curtis, S. Panda, A. Li, H. Xu, Y. Bai, I. Ogihara, E. O’Reilly, Y. Wang, L. Dobbyn, M. Popoli, J. Ptak, N. Nehme, N. Silliman, J. Tie, P. Gibbs, L. T. Ho-Pham, B. N. Tran, T. S. Tran, T. V. Nguyen, E. Irajizad, M. Goggins, C. L. Wolfgang, T. Wang, I. Shih, A. Fader, A. M. L. Lennon, R. H. Hruban, C. Bettegowda, L. Gilbert, K. W. Kinzler, N. Papadopoulos, B. Vogelstein, J. T. Vogelstein, and C. Douville. AI You Can Trust: Applications in Multi-Cancer Early Detection. PNAS, 2025.
  3. Q. Wang, N. Randel, Y. Yin, C. Shand, A. Strange, M. Winding, A. Cardona, M. Zlatic, J. T. Vogelstein, and C. E. Priebe. Measuring the functional complexity of nanoscale connectomes: polarity matters. None, 2025.
  4. K. Konishcheva, B. Leventhal, M. Koyama, S. Panda, J. T. Vogelstein, M. Milham, A. Lindner, and A. Klein. Accurate and efficient data-driven psychiatric assessment using machine learning. PsyArXiv, 2024.
  5. J. Chung, E. W. Bridgeford, M. Powell, D. Pisner, T. Xu, and J. T. Vogelstein. Are human connectomes heritable? bioRxiv, 2024.
  6. C. Shen, S. Panda, and J. T. Vogelstein. Learning Interpretable Characteristic Kernels via Decision Forests. arXiv, 2023.
  7. Eric W. Bridgeford, Jaewon Chung, Brian Gilbert, Sambit Panda, Adam Li, Cencheng Shen, Alexandra Badea, Brian Caffo, and Joshua T. Vogelstein. Learning sources of variability from high-dimensional observational studies. arXiv, 2023.
  8. T. Xu, J. Cho, G. Kiar, E. W. Bridgeford, J. T. Vogelstein, and M. P. Milham. A Guide for Quantifying and Optimizing Measurement Reliability for the Study of Individual Differences. bioRxiv, 2022.
  9. Haoyin Xu, Kaleab A. Kinfu, Will LeVine, Sambit Panda, Jayanta Dey, Michael Ainsworth and Yu-Chung Peng, Madi Kusmanov, Florian Engert, Christopher M. White, Joshua T. Vogelstein, and Carey E. Priebe. When are Deep Networks really better than Decision Forests at small sample sizes, and how? arXiv, 2021.
  10. S. Panda, S. Palaniappan, J. Xiong, E. W. Bridgeford, . Mehta, C. Shen, and J. T. Vogelstein. hyppo: A Multivariate Hypothesis Testing Python Package. arXiv, 2021.