Publication:   Journal of Multiscale Neuroscience                DOI:   https://doi.org/10.56280/1703020960

 

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.

​

Keyword: Bioinformatics, brain complexity, neurodegeneration, integrative multi-omics approaches, neurodevelopmental

​

Conflict of Interest

The authors declare no conflict of interest

                
​Copyright: © 2025 The Author(s). Published by Neural Press.

This is an open access article distributed under the terms and conditions of the CC BY 4.0 license.

​

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, Neural Press™  or the editors, and the reviewers. Any product that may be evaluated in this article, or claim that made by its manufacturer, is not guaranteed or endorsed by the publisher.

 ​

References:

F., Gong, G., Wang, Y., Bian, M., Yu, L., Wei, C., 2017. MiR-124 acts as a target for Alzheimer’s disease by regulating BACE1. Oncotarget 8, 114065–114071.

 

Arrambide, G., Espejo, C., Eixarch, H., Villar, L.M., Alvarez-Cermeño, J.C. et al., 2016. Neurofilament light chain level is a weak risk factor for the development of MS. Neurology 87, 1076–1084.

 

Backhausen, L.L., Herting, M.M., Tamnes, C.K., Vetter, N.C., 2022. Best practices in structural neuroimaging of neurodevelopmental disorders. Neuropsychol Rev 32, 400–418.

 

Bartha, Á., Győrffy, B., 2019. Comprehensive outline of whole exome sequencing data analysis tools available in clinical oncology. Cancers 11, 1725.

 

Blennow, K., Zetterberg, H., 2018. Biomarkers for Alzheimer’s disease: current status and prospects for the future. J Intern Med 284, 643–663.

 

Catanese, A., Rajkumar, S., Sommer, D., Masrori, P., Hersmus, N., Van Damme, P., Witzel, S., Ludolph, A., Ho, R., Boeckers, T.M., Mulaw, M., 2023. Multiomics and machine-learning identify novel transcriptional and mutational signatures in amyotrophic lateral sclerosis. Brain 146, 3770–3782.

​