Artificial Intelligence in genetic healthcare: toward adaptive and systems-level genomic medicine

This project looks at how to use artificial intelligence in genetic health care to move from fixed DNA analysis to a flexible, system‑wide approach. Traditional genomic prediction models usually treat clinical and genetic data as separate, unchanging snapshots. To fix that, we propose a step‑by‑step method that combines time‑series multi‑omics data, medical records, and environmental information. Using explainable AI (XAI) and federated learning, the system captures changing biological relationships, pathway links, and network‑level biomarkers as a disease progresses. The framework also shows how to monitor biological resilience and backup mechanisms before standard clinical markers show any problems. Aimed at B.Tech engineering students, the project helps you build a simulated computational

pipeline that uses machine learning to predict shifts in biological states and support personalized clinical decisions. By cleaning data, creating network models, and applying predictive analytics, the project demonstrates how to set up continuous feedback loops that track patient health over time, providing a solid base for next‑generation health‑monitoring systems.

Message: Hi FE, I need help with “Artificial Intelligence in genetic healthcare: toward adaptive and systems-level genomic medicine” in “Biotechnology & Biomedical Engineering”