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Reconstructing disease dynamics for mechanistic insights and clinical benefit

Medicine and Health

Reconstructing disease dynamics for mechanistic insights and clinical benefit

A. Frishberg, N. Milman, et al.

Dive into the dynamics of disease progression with TimeX, a groundbreaking algorithm developed by leading researchers including Amit Frishberg and Neta Milman. This innovative tool not only reveals key insights into urothelial bladder cancer but also enhances molecular clarity, paving the way for improved patient stratification and outcome prediction.

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Playback language: English
Abstract
Understanding disease progression dynamics is crucial for diagnostics and treatment. This paper introduces TimeX, an algorithm that analyzes high-dimensional, short time-series data to build a comparative framework for capturing disease dynamics. The algorithm's utility is demonstrated through studies of multiple diseases, revealing a stromal pro-invasion point in urothelial bladder cancer associated with immune cell infiltration and increased mortality. TimeX also differentiates between early and late tumors within the same subtype, identifying potential targetable pathways. The approach improves molecular interpretability and offers clinical benefits for patient stratification and outcome prediction.
Publisher
Nature Communications
Published On
Oct 27, 2023
Authors
Amit Frishberg, Neta Milman, Ayelet Alpert, Hannah Spitzer, Ben Asani, Johannes B. Schiefelbein, Evgeny Bakin, Karen Regev-Berman, Siegfried G. Priessinger, Joachim L. Schultze, Fabian J. Theis, Shai S. Shen-Orr
Tags
disease progression
TimeX
urothelial bladder cancer
immune cell infiltration
molecular interpretability
patient stratification
outcome prediction

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