Artificial Intelligence (AI) is catalyzing transformative changes in the sphere of genomic medicine. Advances like genomic sequencing and mass spectrometry have resulted in a surge of molecular data, providing clinicians with the tools for precise diagnoses and targeted treatments.
These developments extend beyond DNA and RNA genetic sequencing, with high-dimensional analysis of proteins and metabolites on the rise.
Analytical tools have concurrently evolved to handle the ‘big data’ explosion. With its iterative, data-driven models, machine learning has proved pivotal in this context.
Deep learning, a specialized form of machine learning involving deep neural networks, has found extensive applications across domains. Now, these techniques are influencing medicine, yielding clinically relevant insights.
Significant progress has been made in the machine learning-based analysis of genomic, transcriptomic, epigenomic, proteomic, and metabolomic data. These analyses have been particularly insightful for rare genetic diseases.
To comprehensively understand AI’s role in genomic medicine, delve into this insightful review by Dr. Bruna Gomes and Euan Ashley, M.B., Ch.B., D.Phil.