This article provides a systematic comparison of data-driven signature models and mechanism-driven theory-based models in biomedical research and drug development.
This article provides a comprehensive resource for researchers and drug development professionals on the identification and validation of age-resilient neural signature biomarkers.
This article explores the integration of leverage score sampling with established feature selection paradigms to address critical challenges in high-dimensional biomedical data analysis, particularly in pharmaceutical drug discovery.
The adoption of complex machine learning (ML) and deep learning (DL) models in biomedical research and drug development is hampered by their 'black box' nature, where internal decision-making processes are...
This article provides a comprehensive framework for the statistical validation of brain connectivity measures at the single-subject level, a critical requirement for personalized diagnostics and treatment monitoring in clinical neuroscience...
This article examines a critical challenge in modern research: the inflation of effect sizes in small discovery datasets and its detrimental impact on replicability.
This article addresses the critical challenge of ensuring the replicability of model fit across varying spatial extents, a pivotal concern for researchers and drug development professionals.
This article provides a comprehensive framework for establishing reproducible brain-phenotype signatures, a critical challenge in neuroscience and neuropharmacology.
This article synthesizes current research on individual-specific brain parcellations and their stability, a frontier in neuroscience with profound implications for precision medicine and pharmaceutical development.
This article presents a comprehensive framework for the systematic extraction and comparison of interpretable signatures from whole-brain dynamics, moving beyond limited, manually-selected statistical properties.