Machine Learning Approaches in Genomics: An In-Depth Review of Predictive and Diagnostic Applications for Genetic Disorders

Abstract

The rapid growth of machine learning (ML) is transforming how we study and interpret the results of genomic data, opening new possibilities for predicting and diagnosing genetic disorders. Genetic disorders have long posed a challenge for medicine, but recent advances in machine learning are giving us powerful new tools to understand and combat these conditions. In this review, we explore the ML techniques from traditional algorithms to advanced deep learning models are being used to analyze genetic variations, improve disease detection, and pave the way for precision medicine. The various research analysis also gives a complete examination of diverse ML techniques that are revolutionizing the field of genetic research. This review also highlights the methods that facilitate the detection of disease-associated genetic variants, improve the accuracy of genomic sequencing, and enable the classification of complex genetic disorders. At the same time, it also addresses challenges like data quality, model interpretability, and ethical concerns that come with integrating AI into genomics. ML algorithm also has an current knowledge to improve the exactness of diagnosis by analyzing the genomic data and identifying the patterns that are associated with diseases.