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A framework for considering prior information in network-based approaches to --omics data analysis
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  • Julia Somers,
  • Madeleine R. Fenner,
  • Dharani Thirumalaisamy,
  • Garth Kong,
  • William Yashar,
  • Meric Kinali,
  • Kisan Thapa,
  • Olga Nikolova,
  • Özgün Babur,
  • Emek Demir
Julia Somers
Oregon Health & Science University
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Madeleine R. Fenner
Oregon Health & Science University
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Dharani Thirumalaisamy
Oregon Health & Science University
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Garth Kong
Oregon Health & Science University
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William Yashar
Oregon Health & Science University
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Meric Kinali
University of Massachusetts Boston
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Kisan Thapa
University of Massachusetts Boston
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Olga Nikolova
Oregon Health & Science University
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Özgün Babur
University of Massachusetts Boston
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Emek Demir
Oregon Health & Science University

Corresponding Author:demire@ohsu.edu

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Abstract

For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of prior knowledge networks is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.
19 Jul 2023Submitted to PROTEOMICS
20 Jul 2023Submission Checks Completed
20 Jul 2023Assigned to Editor
20 Jul 2023Review(s) Completed, Editorial Evaluation Pending
20 Jul 2023Reviewer(s) Assigned
21 Aug 2023Editorial Decision: Revise Minor
20 Sep 2023Review(s) Completed, Editorial Evaluation Pending
20 Sep 20231st Revision Received
21 Sep 2023Editorial Decision: Accept