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“iNETgrate”: integrating DNA methylation and gene expression data in a single gene network

Integrating multi–omics data in one model can increase statistical power. However, designing such a model is challenging because different omics are measured at different levels. We developed the iNETgrate package (https://bioconductor.org/packages/iNETgrate/) that efficiently integrates transcripto...

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Detalles Bibliográficos
Autores principales: Sajedi, Sogand, Ebrahimi, Ghazal, Roudi, Raheleh, Mehta, Isha, Samimi, Hanie, Kazempour, Shiva, Zainulabadeen, Aamir, Docking, Thomas Roderick, Arora, Sukeshi Patel, Cigarroa, Francisco, Seshadri, Sudha, Karsan, Aly, Zare, Habil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Journal Experts 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10462231/
https://www.ncbi.nlm.nih.gov/pubmed/37645739
http://dx.doi.org/10.21203/rs.3.rs-3246325/v1
Descripción
Sumario:Integrating multi–omics data in one model can increase statistical power. However, designing such a model is challenging because different omics are measured at different levels. We developed the iNETgrate package (https://bioconductor.org/packages/iNETgrate/) that efficiently integrates transcriptome and DNA methylation data in a single gene network. Applying iNETgrate on five independent datasets improved prognostication compared to common clinical gold standards and a patient similarity network approach.