ArticleBMC medical informatics and decision making2025
Developing a multiomics data-based mathematical model to predict colorectal cancer recurrence and metastasis.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Applications of large-scale artificial intelligence models in bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- IFNIKB: a type I interferon database for antitumuor immunity studies.Database : the journal of biological databases and curation · 2026Article
- Network-informed deconvolution of bulk immune gene co-expression reveals single-cell programs and spatial organization.Frontiers in immunology · 2026Article
- Integrating prognosis-related genes with immune-related gene signature for development and validation of a survival stratification model for early-stage colorectal cancer.Translational cancer research · 2025Article
- DGHNN: a deep graph and hypergraph neural network for pan-cancer related gene prediction.Bioinformatics (Oxford, England) · 2025Article
- Developing a quantum computing model for sequence annotation of interferon protein.Computational and structural biotechnology journal · 2025Article
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4 authors.
Funding
Abstract
backgroundColorectal 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.
resultsPrevious 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.
conclusionsThe experimental results indicate that our proposed multiomics data-based ensemble learning model effectively predicts the recurrence and metastasis of colorectal cancer.
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