Reconstructing disease dynamics for mechanistic insights and clinical benefit.

ABSTRACT: Diseases change over time, both phenotypically and in their underlying molecular processes. Though understanding disease progression dynamics is critical for diagnostics and treatment, capturing these dynamics is difficult due to their complexity and the high heterogeneity in disease development between individuals. We present TimeAx, an algorithm which builds a comparative framework for capturing disease dynamics using high-dimensional, short time-series data. We demonstrate the utility of TimeAx by studying disease progression dynamics for multiple diseases and data types. Notably, for urothelial bladder cancer tumorigenesis, we identify a stromal pro-invasion point on the disease progression axis, characterized by massive immune cell infiltration to the tumor microenvironment and increa

SUBMITTER: Frishberg A 

PROVIDER: S-EPMC10611752 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature