Computational Biology and Cancer Genomics
Dr. Changde Cheng uses computational and analytical methods to investigate cancer ecology and evolution’s impact on therapeutic resistance. The primary objective is to enhance cancer patient outcomes.
Our laboratory strives to develop high-performance machine-learning tools to gain a comprehensive understanding of cancer biology in patients. We employ high-throughput techniques like single-cell RNA-seq, spatial, multiomics, and perturbation sequencing to study cancer’s origin, progression, and therapeutic resistance. Our research focuses on identifying the cancer cell subpopulations responsible for relapse and understanding how microenvironments influence their response to treatment by decoding communication networks among cells.
Our ultimate goal is to translate these findings into clinical settings, making cancer treatment more precise, minimizing disease recurrence risk, and improving patient outcomes.