Big Data Mining for CNS Diseases Analysis and Treatment: Focusing on Drug Target Discovery

Introduction

Central nervous system (CNS) diseases, including neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease, and multiple sclerosis, represent a major global health challenge. Despite significant research efforts, the complexity and multifactorial nature of these diseases hinder the development of effective treatments. The rise of big data analytics and high-throughput technologies offers new opportunities, particularly in drug target discovery. Multi-omics data from genomics, transcriptomics, proteomics, and other resources (e.g., xQTL, pGWAS, MPRA) have deepened our understanding of disease mechanisms. However, translating these insights into therapeutic targets remains challenging due to the complexity of the nervous system, lack of models, and limited clinical trial success. This Special Issue aims to explore innovative big data mining strategies for CNS disease analysis and drug target identification, highlighting advancements in integrating multi-omics data and computational methods to accelerate drug discovery and overcome resistance mechanisms.

Keywords

CNS diseases, Big data mining, Multi-omics data integration, Drug target discovery, Bioinformatics

Sub-topics

• Identification of novel drug targets through multi-omics integration.
• Development of advanced computational pipelines for multi-omics data integration and analysis.
• Machine learning and AI-driven approaches for target prediction and drug repurposing in CNS diseases.
• Systems biology approaches and molecular network analysis.
• Identification of key signaling pathways and regulatory networks implicated in CNS diseases.
• Development of in silico tools for drug screening and target validation.
• Virtual screening, molecular docking, and drug validation tools.
• Integration of environmental data with multi-omics datasets to refine drug target identification.