GCclassifier : An R package for the prediction of molecular subtypes of gastric cancer
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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Detail(s)
Original language | English |
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Pages (from-to) | 752-758 |
Journal / Publication | Computational and Structural Biotechnology Journal |
Volume | 23 |
Online published | 17 Jan 2024 |
Publication status | Published - Dec 2024 |
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DOI | DOI |
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Publisher's Copyright Statement
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Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85182884242&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(2dc4585b-9716-496e-8102-21cd2dc9208a).html |
Abstract
Gastric cancer (GC) is one of the most commonly diagnosed malignancies, threatening millions of lives worldwide each year. Importantly, GC is a heterogeneous disease, posing a significant challenge to the selection of patients for more optimized therapy. Over the last decades, extensive community effort has been spent on dissecting the heterogeneity of GC, leading to the identification of distinct molecular subtypes that are clinically relevant. However, so far, no tool is publicly available for GC subtype prediction, hindering the research into GC subtype-specific biological mechanisms, the design of novel targeted agents, and potential clinical applications. To address the unmet need, we developed an R package GCclassifier for predicting GC molecular subtypes based on gene expression profiles. To facilitate the use by non-bioinformaticians, we also provide an interactive, user-friendly web server implementing the major functionalities of GCclassifier. The predictive performance of GCclassifier was demonstrated using case studies on multiple independent datasets. © 2024 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
Research Area(s)
- Cancer subtyping, Gastric cancer, GCclassifier, R package, Shiny application
Citation Format(s)
GCclassifier: An R package for the prediction of molecular subtypes of gastric cancer. / Li, Jiang; He, Lingli; Zhang, Xianrui et al.
In: Computational and Structural Biotechnology Journal, Vol. 23, 12.2024, p. 752-758.
In: Computational and Structural Biotechnology Journal, Vol. 23, 12.2024, p. 752-758.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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