As the cost of high-throughput sequencing goes down, substantial volumes of biological and medical data have been produced from various sequencing platforms at multiple molecular levels, including genome, transcriptome, proteome, epigenome, metabolome, and so on. For a long time, data analysis on the single molecular level has paved the way to answer many important research questions. However, many neurodegenerative diseases (NDs) involve interactions of molecules from multiple molecular levels, in which conclusions based on single molecular levels are usually incomplete and sometimes misleading. In these scenarios, multi-omics data analysis has unprecedentedly helped capture much more useful information for the diagnosis, treatment, prognosis, and drug discovery of NDs. The first step towards a multi-omics analysis is to establish reliable and robust multi-omics datasets. In the past few years, important ND-associated multi-omics databases like Allen Brain have been constructed, which raised immediate needs like data curation, normalization, interpretation, and visualization for integrative multi-omics explorations. Although there have been several well-established multi-omics databases for NDs like Alzheimer’s disease, similar databases for other NDs are still in urgent need.

After the databases establish, many computational tools and experiential strategies should be developed specifically for them. First, the multi-omics data are usually extremely noisy, complex, heterogeneous, and in high dimension, which presents the need for appropriate denoising and dimension reduction methods. Second, since the multi-omics and non-omics data like pathological and clinical data are usually in different data spaces, a useful algorithm to map them into the same data space and integrate them is nontrivial. In the multi-omics era, there are numerous data-centric tools for the integration of multi-omics datasets, which could be generally divided into three categories: unsupervised, supervised, and semi-supervised methods. Commonly used algorithms include but are not limited to the Bayesian-based method, network-based methods, multi-step analysis methods, and multiple kernel learning methods. Third, methods are needed for studying and verifying the association between two or more levels of multi-omics data and non-omics data. For example, expression quantitative trait loci (eQTL) analysis is widely used to infer the association between a single nucleotide polymorphism (SNP) and the expression of a gene. Recently, the association between omics data and more complex data like pathological and clinical imaging data has been a hot research topic. The outcomes may reveal the underlying molecular mechanism and promote de novo drug design as well as drug repurposing for NDs.

This Special Issue welcomes investigators to share their original research and review articles related to multi-omics studies of NDs, which can be applied for better diagnosis, treatment, prognosis, and drug discovery of human diseases in the future era of precision medicine.

Potential topics include but are not limited to the following:

• Methods for integrating, interpreting, or visualizing two or more omics data
• Methods for identifying interactions between different data modalities
• Methods for disease subtyping and biomarker prediction
• Machine learning or deep learning methods on dimensional reduction and feature selection for big noisy data
• Methods for studying the association among different omics data or between omics and non-omics data like clinical, pathological, and imaging data
• Review of multi-omics resources about NDs
• Experimental validation of biomarkers identified from multi-omics data analysis
• Disease diagnosis and prognosis prediction from imaging and/or non-imaging data analysis
• Clinical application or validation of findings from multi-omics data analysis