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bioRxiv · 10.1101/2025.06.12.659259

SurfacOmics: an R shiny application integrating variable Feature Selection for gene biomarker discovery using Elastic-Net Regularization

Tripathi, S.·Kajla, P.·Abbasi, B. A.·Kota, K. P.·Bailey, A.·Varma, B.
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Abstract

Affordable sequencing technologies have resulted in a rapid rise in genomic and proteomic data. As a result, a massive amount of data is being analyzed, and the outcomes must be summarized as relevant clinical biomarkers. One of the major challenges is enabling wet-lab researchers to make meaningful inferences from the data, even in the absence of expertise in pipeline development and statistical training. We present a user-friendly R shiny application, SurfacOmics, which allows the user to perform biomarker identification via penalized regression algorithms. It also enables researchers to choose a crucial binary variable, such as treatment or group, from the study design metadata to anticipate potential biomarkers. We have introduced a novel concept of scoring scheme to characterize potential biomarkers for prediction, prioritization, and ranking. The tool integrates Gene Ontology information for sub-cellular localization together with a manually curated knowledgebase to provide valuable insights on biological processes, molecular functions and antibody resources, offering a comprehensive view in a single interface.

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Tripathi, S., Kajla, P., Abbasi, B. A., Kota, K. P., Bailey, A., Varma, B.. 2025-06-17. SurfacOmics: an R shiny application integrating variable Feature Selection for gene biomarker discovery using Elastic-Net Regularization. https://doi.org/10.1101/2025.06.12.659259

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