Network-based computational pipeline for studying variability of transcriptome profiles for human diseases

• Autorzy: Cakir Eda , Hütt Marc-Thorsten

Identyfikatory

DOI

10.7494/csci.2025.26.SI.7067

• Języki publikacji: EN

Abstrakty

EN

Machine learning applications to high-throughput data in medicine – one ofthe biggest resources for understanding complex diseases – have been limitedthus far. Here, we present a computational approach for assessing the intrinsicvariability in the most prominent data type, transcriptomics data for diseasecohorts. Our study looks at situations where multiple data sets for the samedisease are available. We leverage concepts of network medicine to assess howthe match between a biological network and a set of differentially expressedgenes varies across different networks and experiments. Our results showedthat different biological networks yielded markedly different results; also, theclustering of diseases depended strongly on the choice of the parameters thatwere contained in the data analysis and network processing.

Słowa kluczowe

EN

transcriptomics data; network medicine; disease cohorts

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2025
• Tom: T. 26 (SI)
• Strony: 69--91
• Opis fizyczny: Bibliogr. 70 poz., rys., tab., wykr.

Twórcy

autor

Cakir Eda

• Constructor University, School of Science, Campus Ring 1 28759 Bremen, Germany

autor

Hütt Marc-Thorsten

• Constructor University, School of Science, Campus Ring 1 28759 Bremen, Germany

Bibliografia

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