Recent Advances in ML Models and Their Applications in Bioinformatics and Biomedical Engineering

The explosion of biological and biomedical data has opened up incredible possibilities for improving healthcare and understanding life itself. Machine learning (ML) has become a game-changer, helping us analyze complex datasets, predict diseases, and design personalized treatments. But it’s not all smooth sailing, integrating ML into bioinformatics and biomedical engineering comes with its fair share of challenges. For instance, many advanced ML models are like “black boxes,” making it hard to trust their decisions in critical areas like clinical diagnostics. Combining different types of biological data, such as genomics and proteomics, is another tough nut to crack. Add to that ethical concerns around data privacy and the sheer computational power needed to process massive datasets, and it’s clear we have work to do. This review dives into these challenges, exploring how cutting-edge ML models like deep learning, reinforcement learning, and graph neural networks, are being used to decode genomes, automate medical imaging, speed up drug discovery, and even monitor health in real-time through wearable devices. It also proposed ways to make ML models more interpretable, integrate diverse biological data seamlessly, and ensure data privacy through federated learning. By tackling these challenges and fostering collaboration across disciplines, this work aims to make ML-driven healthcare solutions not only more effective but also fair and accessible to everyone. Together, we can unlock the full potential of ML to transform healthcare and improve lives worldwide.