Work

Developing a multiomics data-based mathematical model to predict colorectal cancer recurrence and metastasis

Year: 2025

Type: article

Abstract: BACKGROUND: Colorectal cancer is the fourth most deadly cancer, with a high mortality rate and a high probability of recurrence and metastasis. Since continuous examinations and disease monitoring for patients after surgery are currently difficult to perform, it is necessary for us to develop a predictive model for colorectal cancer metastasis and recurrence to improve the survival rate of patients. RESULTS: Previous studies mostly used only clinical or radiological data, which are not sufficient to explain the in-depth mechanism of colorectal cancer recurrence and metastasis. Therefore, this study proposes such a multiomics data-based predictive model for the recurrence and metastasis of colorectal cancer. LR, SVM, Naïve-bayes and ensemble learning models are used to build this predictive model. CONCLUSIONS: The experimental results indicate that our proposed multiomics data-based ensemble learning model effectively predicts the recurrence and metastasis of colorectal cancer.

Source: BMC Medical Informatics and Decision Making

DOI: 10.1186/s12911-025-03012-9

Authors: Bing Li, Ming Xiao, Rong Zeng, Le Zhang

Institutions: Sichuan University, Chinese Academy of Sciences, ShanghaiTech University, Center for Excellence in Molecular Cell Science, University of Chinese Academy of Sciences

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,990