Cancer Genomics

All research related to Cancer Genomics. A full list of topics is available on my research page.

  1. ⭐ Minimizing and quantifying uncertainty in AI-informed decisions: Applications in medicine

    Samuel D. Curtis*, Sambit Panda*, Adam Li*, Haoyin Xu, Yuxin Bai, Itsuki Ogihara, Eliza O’Reilly, Yuxuan Wang, Lisa Dobbyn, Maria Popoli, Janine Ptak, Nadine Nehme, Natalie Silliman, Jeanne Tie, Peter Gibbs, Lan T. Ho-Pham, Bich N. H. Tran, Thach S. Tran, Tuan V. Nguyen, Ehsan Irajizad, Michael Goggins, Christopher L. Wolfgang, Tian-Li Wang, Ie-Ming Shih, Amanda Fader, Anne Marie Lennon, Ralph H. Hruban, Chetan Bettegowda, Lucy Gilbert, Kenneth W. Kinzler, Nickolas Papadopoulos, Bert Vogelstein, Joshua T. Vogelstein, Christopher Douville
    PNAS, 2025

    Introduces MIGHT, which helps quantify the amount of predictive information in very high-dimensional data. This was then used to develop and evaluate a biomedical assay to detect cancer early.

  2. 📄 Fragmentation signatures in cancer patients resemble those of patients with vascular or autoimmune diseases

    Samuel D. Curtis, Tingshan Liu, Yuxin Bai, Yuxuan Wang, Sambit Panda, Adam Li, Haoyin Xu, Eliza O’Reilly, Lisa Dobbyn, Maria Popoli, Janine Ptak, Natalie Silliman, Chris Thoburn, Jeanne Tie, Peter Gibbs, Lan T. Ho-Pham, Bich N. H. Tran, Thach S. Tran, Tuan V. Nguyen, Maximilian F. Konig, Michelle Petri, Antony Rosen, Christopher A. Mecoli, Ami A. Shah, Frits Mulder, Nick van Es, PLATO-VTE Study Group, Chetan Bettegowda, Kenneth W. Kinzler, Nickolas Papadopoulos, Joshua T. Vogelstein, Bert Vogelstein, Christopher Douville
    PNAS, 2025

    Shows that there is a shared inflammatory process between cancer and other diseases and thus uncovers a major reason for false positives in early detection tests for cancer.

  3. 🎓 Random Forest for Hypothesis Testing: Development and Application to Cancer Detection

    Sambit Panda
    Johns Hopkins, 2024

    My PhD thesis, which discusses: (1) how to do k-sample testing via independence testing, (2) creation of the KMERF test, which uses the random forest induced kernel, and (3) introduce MIGHT and CoMIGHT that quantify information within datasets and apply it to a cancer dataset.