This paper examines the analysis of the different data types within the field of Bioinformatics. The aim is to study and handpick machine learning algorithms th

This paper proposes a novel three-layered software architecture for processing and analyzing sequences, medical images, and patient-reported outcomes (PROs). The aim is to provide a comprehensive end-to-end solution for patient diagnosis by integrating all major types of bioinformatics data. This approach leverages advanced machine learning algorithms, including decision trees, support vector machines, neural networks, and quantum neural networks, to enhance the efficiency and effectiveness of precision medicine. A graphical user interface was constructed to validate the suggested approach and present the experimental results.