Authors: Satish Swamy
Abstract: The advent of high-throughput sequencing technologies has revolutionized genomics by generating massive volumes of data, uncovering the genetic basis of complex diseases. However, the sheer complexity and dimensionality of genomic data pose substantial challenges for traditional analytical methods. Artificial intelligence (AI), particularly machine learning and deep learning, provides powerful tools to analyze, interpret, and integrate genomic data to unravel the intricate genetic architecture of complex diseases. This paper explores AI methodologies applied in genomic data analysis, focusing on variant calling, functional annotation, gene-gene interactions, and disease risk prediction. It examines current applications, challenges such as data heterogeneity and model interpretability, and discusses future perspectives in advancing precision medicine.
DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.555
