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Multi-layer Trajectory Clustering: a Network Algorithm for Disease Subtyping.

Sanjukta Krishnagopal^1,2

¹Department of Physics, University of Maryland, College Park, Maryland, 20742, United States of America.

Biomedical Physics & Engineering Express

|January 20, 2022

Summary

This summary is machine-generated.

This study introduces a novel network-based Trajectory Clustering (TC) algorithm to identify Parkinson's disease subtypes. The algorithm reveals distinct patient trajectories, aiding personalized medicine and disease prognosis.

Keywords:

Parkinson's disease disease modeling multi-layer network network evolution network medicine predictive medicine trajectory clustering

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Area of Science:

Biomedical informatics
Computational biology
Network science

Background:

Disease heterogeneity suggests underlying subtypes impacting progression and treatment.
Identifying disease subtypes is crucial for personalized medicine and accurate prognosis.
Current methods may not fully capture complex disease progression patterns.

Purpose of the Study:

To develop a novel data-driven, network-based Trajectory Clustering (TC) algorithm for identifying Parkinson's disease subtypes.
To model patient-variable interactions and cluster similar disease trajectories.
To leverage network analysis for understanding disease heterogeneity and progression.

Main Methods:

Modeled patient-variable interactions as a bipartite network.
Extracted co-expressing disease variable communities at different progression stages.
Clustered patient trajectories using a multi-layer network considering direct and second-order similarities.
Utilized temporal and disease severity layers to define subtypes.

Main Results:

Identified four distinct Parkinson's disease subtypes based on temporal progression and disease severity.
Temporal subtypes showed variations in the progression of different disease domains (e.g., cognitive, mental health).
Severity-based subtypes demonstrated differing degrees of disease progression over five years.

Conclusions:

The Trajectory Clustering (TC) algorithm effectively identifies Parkinson's disease subtypes.
The identified subtypes align with existing medical literature, validating the method's robustness.
This generalizable approach can be applied to other progressive diseases for subtype-specific treatments.