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July 29, 2025Computer ScienceOpen Access

A Network-Based Computationalpipeline to Study the Variability Oftranscriptome Profiles for Humandiseases

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Key Points

Different biological networks yield markedly different results for analyzing disease cohorts, affecting conclusions.
Machine learning tools applied to transcriptome data reveal significant variability across datasets for diseases.
Using a computational approach, this work shows the clustering of diseases is highly dependent on data analysis parameters.
Leveraging network medicine concepts highlights how approaches can shift understanding of gene expression in diseases.

Implication

This computational approach assesses transcriptome variability in disease cohorts, suggesting differing biological network insights.

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Cite This Study

Eda Cakir (2025) studied this question.

synapsesocial.com/papers/68c1ad6a54b1d3bfb60e5f30 https://doi.org/10.7494/csci.2025.26.si.7067

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Network‐based precision medicine and systems pharmacology2025 · 9 citations
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3Computational Systems Biology Methods for Cross‐Disease Comparison of Omics Data2025 · 7 citations
4Paired-Sample and Pathway-Anchored MLOps Framework for Robust Transcriptomic Machine Learning in Small Cohorts (Preprint)2025
5Computational protocol to identify shared transcriptional risks and mutually beneficial compounds between diseases2024

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