Advancements in Machine Learning for Predictive Modeling of Genetic Disorders

Detecting and understanding gene patterns before the onset of genetic diseases holds immense promise for prevention. This paper discusses the current state of machine learning techniques available for the disease diagnosis problem, focusing on existing approaches, their analysis, and possible future work. Machine learning algorithms can process large amounts of genetic information such as DNA sequences, gene expression data, and images to identify and recognize patterns that could be useful in predicting disease risk or its progression. In this regard, the paper also focuses on a sequence comparison method using protein-level similarity, leveraging codon-based translation from DNA to protein sequences to calculate similarity indices between a test sequence and multiple disease related sequences. This shows how machine learning and bioinformatics convergence can bring remarkable progress in predictive analytics.