Applications of Deep Learning and Fuzzy Systems to Detect Cancer Mortality in Next-Generation Genomic Data

In the era of advanced precision medicine, next-generation genomic data are crucial to achieve breakthroughs in cancer medicine. Effective cancer mortality risk estimation for genomic data associated with cancer remains a vital challenge. The combination of machine learning algorithms and conventional survival analysis can advance the detection of high-risk missense mutation variants and candidate genes associated with cancer mortality in next-generation genomic data. In this article, a fuzzy logic system combined with machine learning algorithms and conventional survival analysis named FuzzyDeepCoxPH was proposed to identify high-risk missense mutation variants and candidate genes highly associated with cancer mortality. DL-derived abstracted weights and Cox proportional hazards (CoxPH) ratios were used to develop four model-based risk scores to consider the factor importance associated with risk stratification, time-varying effects, and individual and interaction effects among features. Fuzzy rules based on a fuzzy logic system were designed to integrate these considerations by merging four model-based risk scores to develop advanced risk estimation. The clinical features and next-generation sequencing of deoxyribonucleic acid and ribonucleic acid genomic data were used to evaluate FuzzyDeepCoxPH performance. The results indicated that FuzzyDeepCoxPH can effectively distinguish high-risk variants and candidate genes related to cancer mortality. In FuzzyDeepCoxPH, the fuzzy logic system was applied to combine DL-based and CoxPH-based models to provide a comprehensive cancer mortality risk estimation for cancer medicine.

Automated summary

In this study, a method named FuzzyDeepCoxPH that combines machine learning and traditional survival analysis was proposed to identify high-risk missense mutation variants and candidate genes associated with cancer mortality. The results showed that FuzzyDeepCoxPH can effectively distinguish high-risk variants and candidate genes related to cancer mortality, providing comprehensive risk estimation for cancer medicine.