Intelligent and effective informatic deconvolution of "Big Data" and its future impact on the quantitative nature of neurodegenerative disease therapy

Abstract

Biomedical data sets are becoming increasingly larger and a plethora of high-dimensionality data sets ("Big Data") are now freely accessible for neurodegenerative diseases, such as Alzheimer's disease. It is thus important that new informatic analysis platforms are developed that allow the organization and interrogation of Big Data resources into a rational and actionable mechanism for advanced therapeutic development. This will entail the generation of systems and tools that allow the cross-platform correlation between data sets of distinct types, for example, transcriptomic, proteomic, and metabolomic. Here, we provide a comprehensive overview of the latest strategies, including latent semantic analytics, topological data investigation, and deep learning techniques that will drive the future development of diagnostic and therapeutic applications for Alzheimer's disease. We contend that diverse informatic "Big Data" platforms should be synergistically designed with more advanced chemical/drug and cellular/tissue-based phenotypic analytical predictive models to assist in either de novo drug design or effective drug repurposing.

Keywords: Aging; Alzheimer's disease; Big data; Genomics; High-dimensionality; Informatics; Metabolomics; Molecular signature; Proteomics; Transcriptomics.