A novel computational framework for integrating multidimensional data to enhance accuracy in predicting the prognosis of colorectal cancer

A novel computational framework for integrating multidimensional data to enhance accuracy in predicting the prognosis of colorectal cancer

Abstract Accurate prognosis prediction is the key to achieving precision treatment and guiding the selection of adjuvant chemotherapy in high‐risk stage II/III colorectal cancer (CRC) patients. Here we developed a novel machine learning method, the random non‐negative matrix factorization (RNMF) alg...

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Bibliographic Details
Main Authors: Qinran Zhang, Yuhong Xu, Shiyang Kang, Junquan Chen, Zhihao Yao, Haitao Wang, Qinian Wu, Qi Zhao, Qihua Zhang, Rui‐hua Xu, Xiufen Zou, Huiyan Luo
Format: Article
Language:English
Published: Wiley 2022-09-01
Series:MedComm – Future Medicine
Subjects:
biomarker
multidimensional data integration
prognosis prediction
stage II/III colorectal cancer
the random non‐negative matrix factorization algorithm
Online Access:https://doi.org/10.1002/mef2.27

Internet

https://doi.org/10.1002/mef2.27

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