Abstract

Brain disorders, which encompass neurodevelopmental conditions and age-related neurodegenerative diseases, are becoming more prevalent due to population growth, increased life expectancy, and enhanced diagnostic capabilities. Despite clinical differences, these diseases share complex molecular alterations across various omics layers, including dysregulation of gene expression, epigenetic modifications, gene mutations, and alterations in protein networks. Today, we can directly quantify those alterations; however, determining their relationship to brain function and identifying characteristic disease patterns through the integration of these biomarkers remains a significant challenge. Bioinformatics has become crucial in understanding these signals, enabling us to interpret what they reveal about the mechanisms that regulate our physiology and its pathological changes through the integration of multi-omics data. By analyzing the interplay between genetic, transcriptomic, and epigenetic factors, we can reconstruct disease-specific networks and find potential links between early stages of brain development and degenerative processes. Given the vast amounts of molecular data available today and the significant influx of artificial intelligence techniques, it is increasingly important to develop methods that can explore these datasets deeply and extract the hidden information within them. This review, which focuses specifically on neuroscience-associated molecular biomarkers and the main methods used to acquire and analyze them, highlights how computational approaches are broadening our understanding of brain disorders, opening new avenues for early diagnosis, the development of personalized therapies, and a novel perspective on brain health.