查看文章

SNPs (single nucleotide polymorphisms) are the most common biomarkers, and they have a wide range of applications, especially in human genetics. The high cost of genotyping many SNPs is one of the most significant obstacles in SNP analysis. Selection and classification of characteristics are particularly tough due to these challenges. Therefore, a method for detecting significant SNPs and distinguishing healthy and ill samples has been developed. Finding the (sub) optimal subset of attributes is a Nondeterministic Polynomial Time (NP) hard problem. To begin, the nominal SNP data is transformed to numeric using an intelligence method called Mean Encoding. After that, a Genetic Algorithm (GA)-based feature selection is applied, which selects the important traits while discarding the redundant ones. Finally, the recommended Self-organizing Auto-Encoder (SOAE) is utilized to categorize, allowing it to create …