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

Abstract

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) algorithm, which outperformed traditional non‐negative matrix factorization in terms of computational speed, accuracy, and robustness in simulated data sets. Moreover, based on multidimensional data from CRC patients from The Cancer Genome Atlas database and DNA methylation data from those from Sun Yat‐sen University cancer center, we further demonstrated the excellent performance of a novel prognostic computational framework based on the RNMF (PCF_RNMF), which is capable of integrating multidimensional training while allowing survival prediction when single dimensional data for validation is provided. This novel algorithm has great potential to mitigate the challenge of integrating various types of data in public databases with clinically available single‐dimensional data to allow cost‐effective survival prediction for CRC patients in clinical practice.

Keywords

• biomarker
• multidimensional data integration
• prognosis prediction
• stage II/III colorectal cancer
• the random non‐negative matrix factorization algorithm