Identification of mortality-risk-related missense variant for renal clear cell carcinoma using deep learning.

Introduction: Kidney renal clear cell carcinoma (KIRCC) is a highly heterogeneous and lethal cancer that can arise in patients with renal disease. DeepSurv combines a deep feed-forward neural network with a Cox proportional hazards function and could provide optimized survival results compared with convenient survival analysis.

Methods: This study used an improved DeepSurv algorithm to identify the candidate genes to be targeted for treatment on the basis of the overall mortality status of KIRCC subjects.

Identification of High-Order Single-Nucleotide Polymorphism Barcodes in Breast Cancer Using a Hybrid Taguchi-Genetic Algorithm: Case-Control Study.

Background: Breast cancer has a major disease burden in the female population, and it is a highly genome-associated human disease. However, in genetic studies of complex diseases, modern geneticists face challenges in detecting interactions among loci.

Objective: This study aimed to investigate whether variations of single-nucleotide polymorphisms (SNPs) are associated with histopathological tumor characteristics in breast cancer patients.

PBMDR: A particle swarm optimization-based multifactor dimensionality reduction for the detection of multilocus interactions.

Studies on multilocus interactions have mainly investigated the associations between genetic variations from the related genes and histopathological tumor characteristics in patients. However, currently, the identification and characterization of susceptibility genes for complex diseases remain a great challenge for geneticists. In this study, a particle swarm optimization (PSO)-based multifactor dimensionality reduction (MDR) approach was proposed, denoted by PBMDR.