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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

SWEET: a single-sample network inference method for deciphering individual features in disease.

Hsin-Hua Chen¹, Chun-Wei Hsueh¹, Chia-Hwa Lee^2,3,4

¹Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan.

Briefings in Bioinformatics

|January 31, 2023

Summary

This summary is machine-generated.

We developed a new method called SWEET to build accurate single-sample networks (SINs) from gene expression data. This approach improves personalized cancer diagnosis and treatment by revealing individual patient characteristics and identifying potential drug targets.

Keywords:

drug repurposing gene expression network inference network medicine precision medicine single-sample network

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Area of Science:

Genomics
Systems Biology
Network Medicine

Background:

Accurate construction of single-sample networks (SINs) is crucial for personalized diagnostics and therapeutics.
Current methods struggle to capture individual patient characteristics and disease heterogeneity.

Purpose of the Study:

To introduce the sample-specific-weighted correlation network (SWEET) method for improved SIN inference.
To enhance the characterization of individual biological systems and disease subtypes.

Main Methods:

Developed the SWEET method integrating genome-wide sample-to-sample correlation with differential network analysis.
Assessed genome-wide sample weights without prior subpopulation knowledge to mitigate bias.
Validated SWEET SINs against scale-free properties, human interactomes, and cancer gene identification.

Main Results:

SWEET SINs demonstrated superior scale-free properties and overlap with human interactomes across 16 cancers.
The method effectively identified cancer-related genes, somatic mutations, mut-drivers, and essential genes.
Identified two candidate drugs (albendazole, encorafenib) and two LUAD subtypes with distinct features.

Conclusions:

SWEET offers a robust approach for SIN inference, complementing existing methods.
The method provides valuable insights for network medicine and precision medicine applications.
SWEET facilitates the identification of novel therapeutic strategies and disease subtypes.