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DynProfiler: a Python package for comprehensive analysis and interpretation of signaling dynamics leveraged by deep

Masato Tsutsui^1,2, Mariko Okada¹

¹Institute for Protein Research, Osaka University, Suita 565-0871, Japan.

Bioinformatics Advances

|October 11, 2024

Summary

DynProfiler uses deep learning to analyze biological signaling dynamics for disease biomarkers. This tool extracts features from simulations to predict patient mortality risk and identify key biological pathways.

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Area of Science:

Computational Biology
Systems Biology
Artificial Intelligence in Medicine

Background:

Biological signaling dynamics are crucial for understanding disease mechanisms.
Simulated signaling dynamics are emerging as potential biomarkers.
Traditional methods for analyzing these dynamics often require manual feature selection.

Purpose of the Study:

To develop a deep learning-based tool, DynProfiler, for extracting informative features from biological signaling dynamics without manual feature selection.
To incorporate explainable AI for quantitative, time-dependent importance scores of dynamics.
To demonstrate DynProfiler's utility in predicting mortality risk and identifying biomarkers in breast cancer.

Main Methods:

Utilized deep learning techniques to process entire signaling dynamics, including intermediate variables, as input.
Employed an explainable AI solution to provide time-dependent feature importance scores.
Applied DynProfiler to simulated breast cancer signaling dynamics data.

Main Results:

DynProfiler successfully extracted high-quality features from simulated breast cancer dynamics.
Extracted features were effective in predicting mortality risk.
Identified upregulated phosphorylated GSK3β as a significant biomarker for poor prognosis.

Conclusions:

DynProfiler offers a label-free deep learning approach for analyzing biological signaling dynamics.
The tool provides valuable insights for clinical applications, including patient stratification and survival prediction.
DynProfiler aids in elucidating complex biological system dynamics and identifying novel biomarkers.