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GCclassifier: An R package for the prediction of molecular subtypes of gastric cancer.
Li, Jiang; He, Lingli; Zhang, Xianrui; Li, Xiang; Wang, Lishi; Zhu, Zhongxu; Song, Kai; Wang, Xin.
Afiliação
  • Li J; Department of Surgery, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China.
  • He L; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China.
  • Zhang X; Department of Biomedical Sciences, City University of Hong Kong, Hong Kong Special Administrative Region of China.
  • Li X; Department of Surgery, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China.
  • Wang L; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China.
  • Zhu Z; Department of Biomedical Sciences, City University of Hong Kong, Hong Kong Special Administrative Region of China.
  • Song K; Department of Surgery, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China.
  • Wang X; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China.
Comput Struct Biotechnol J ; 23: 752-758, 2024 Dec.
Article em En | MEDLINE | ID: mdl-38304548
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Comput Struct Biotechnol J Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Comput Struct Biotechnol J Ano de publicação: 2024 Tipo de documento: Article