Fewer Biomarkers

Research on "fewer biomarkers" focuses on identifying smaller, more effective sets of biomarkers for disease diagnosis and prognosis, aiming to improve efficiency and reduce the complexity of existing methods. Current efforts leverage machine learning algorithms, including neural networks (e.g., Neural Additive Models, cycle-GANs, TRI-LSTM, XGBoost), and Bayesian networks to analyze diverse data types such as gene expression, speech patterns, and medical images, often integrating multiple data modalities. This streamlined approach promises to accelerate biomarker discovery, enhance diagnostic accuracy, and ultimately lead to more personalized and effective healthcare interventions.