Survival prediction for prostate cancer using machine learning and bioinformatics models

Keyphrases

• Machine Learning Models 100%
• Prostate Cancer 100%
• Survival Prediction 100%
• Bioinformatics Model 100%
• Transcriptomic Data 66%
• Functional Studies 33%
• Univariate Analysis 33%
• Disease Management 33%
• Multivariate Analysis 33%
• Clinical Data 33%
• Therapeutic Target 33%
• Potential Therapeutics 33%
• Treatment Decisions 33%
• Long-term Survival 33%
• Genetic Influences 33%
• Malignancy 33%
• Prostate Cancer Patients 33%
• Cox Proportional Hazards Model 33%
• TGFBR3 33%
• Molecular Pathways 33%
• Treatment Planning 33%
• Disease Mechanisms 33%
• Gene number 33%
• Caveolin-1 (Cav-1) 33%
• Genetic Determinants 33%
• Hub Genes 33%
• Functional Enrichment Analysis 33%
• Cancer-associated 33%
• Fist 33%
• Survival Period 33%
• Overall Survival 33%
• Cancer Genome Atlas 33%
• Signature Genes 33%
• TUBB4A 33%
• ITGB4 33%
• RhoU 33%
• EFNB1 33%
• TPM2 33%
• Prostate Cancer Survival 33%
• Prostate Cancer Development 33%
• MYL9 33%
• Therapeutic Interventions 33%
• Protein-protein Interaction Analysis 33%
• Management Treatments 33%
• MiRNA Transcriptome 33%

Biochemistry, Genetics and Molecular Biology

• Genetics 100%
• Survival Prediction 100%
• Bioinformatics 100%
• RNA Sequencing 50%
• Multivariate Analysis 50%
• Hub Genes 50%
• TPM2 50%
• Transcriptomics 50%
• Decision Making 50%
• Protein-Protein Interaction 50%
• Survival Time 50%
• Long Term Survival 50%
• Overall Survival 50%
• Cancer Survival 50%
• EFNB1 50%