📄 Abstract
The identification of genetic markers such as single nucleotide polymorphisms (SNPs) plays a critical role in understanding disease susceptibility and guiding personalized medicine. Recent advances in machine learning (ML) have provided new methods to address the complexities of genetic data. This paper introduces a novel ensemble learning technique that integrates attention-based neural networks with traditional random forest algorithms to enhance the identification of SNPs linked to disease outcomes. Using a benchmark dataset of genome-wide association studies (GWAS), we demonstrate how the proposed method improves prediction accuracy and model interpretability, thereby offering potential applications in clinical genomics.
📚 How to Cite:
Mr. Sudeep M S, Mr. Veeresh A C, Mrs. Anitha J , GENOMICA AI- ENHANCING GENETIC MARKER IDENTIFICATION THROUGH MACHINE LEARNING , Volume 10 , Issue 9, september 2024, EPRA International Journal of Multidisciplinary Research (IJMR) ,