Biomarker Selection

Biomarker selection aims to identify measurable indicators that reliably predict disease presence, progression, or treatment response, ultimately enabling personalized medicine. Current research emphasizes developing robust and efficient methods for biomarker discovery using machine learning, including deep learning architectures like convolutional neural networks (for image-based biomarkers) and graph neural networks (for omics data), as well as ensemble methods and techniques that incorporate prior biological knowledge. This field is crucial for advancing diagnostics, prognostics, and therapeutics across various diseases, improving patient outcomes through more precise and targeted interventions. The development of interpretable models is also a key focus, enhancing clinical trust and facilitating integration into healthcare workflows.