A hierarchical clustering approach for colorectal cancer molecular subtypes identification from gene expression data

Author:

Raghav Shivangi,Suri Aastha,Kumar DeepikaORCID,Aakansha Aakansha,Rathore Muskan,Roy SudiptaORCID

Publisher

Elsevier BV

Subject

Health Informatics,Medicine (miscellaneous),Biomedical Engineering,Artificial Intelligence

Reference31 articles.

1. Molecular subtypes of colorectal cancer: an emerging therapeutic opportunity for personalized medicine;Singh;Genes Dis,2019

2. World Health Organization. Global cancer rates could increase by 50% to 15 million by 2020. Available from https://www.who.int/news/item/03-04-2003-global-cancer-rates-could-increase-by-50-to-15-million-by-2020.

3. Practical and robust identification of molecular subtypes in colorectal cancer by immunohistochemistry;Trinh;Clin Cancer Res,2017

4. Consensus molecular subtypes of colorectal cancer are recapitulated in in vitro and in vivo models;Linnekamp;Cell Death Differ,2018

5. Gene encoder: a feature selection technique through unsupervised deep learning-based clustering for large gene expression data;Uzma;Neural Comput Applic,2022

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