Deep CNN based Genomic variant classifier To predict Disease Susceptibility

Abstract

The rapid advancement in genomics has deepened our understanding of genetic variations and their link to human diseases. Genomic variants, including single nucleotide polymorphisms (SNPs), insertions, deletions, and complex rearrangements, influence disease susceptibility. Predicting disease risk based on these variants is crucial for personalized medicine. Historically, disease prediction relied on family history, linkage studies, and statistical models, which were slow, error-prone, and lacked accuracy. Before artificial intelligence, heuristic methods struggled with large genomic datasets, limiting predictive power. The growing need for precision medicine and the surge in genomic data demand automated, scalable solutions for accurate variant interpretation. Complex diseases such as cancer and cardiovascular disorders require advanced computational techniques. Traditional methods face challenges, including limited predictive accuracy, reliance on human expertise, and difficulty handling vast genomic data from next-generation sequencing. Addressing these issues requires robust, high-precision computational systems. This system utilizes deep convolutional neural networks (CNNs) to classify genomic variants and predict disease susceptibility with high accuracy. Deep learning automatically extracts features from large datasets, eliminating extensive manual feature engineering. By analyzing genomic sequences, it identifies disease-associated variants with improved precision, surpassing previous approaches. This AI-driven model enhances genomic variant analysis by reducing human intervention, efficiently processing large data, and delivering timely, accurate predictions. Its deep learning foundation revolutionizes disease risk assessment, paving the way for personalized medicine and early interventions. This breakthrough promises improved healthcare outcomes by enabling more precise, individualized treatment strategies.