Abstract:

Neurodegenerative diseases, including Alzheimer’s and Parkinson’s, represent a growing global health challenge. This article delves into the potential of predictive analysis, powered by big data analytics and artificial intelligence, to revolutionize early diagnosis and interventions. Leveraging robust ex vivo transcriptomics datasets, we identify transcriptomic signatures that can serve as biomarkers for these diseases. We explore both linear models like LASSO and non-linear deep learning approaches to uncover complex genetic patterns. Our findings demonstrate the power of machine learning in enhancing our understanding of neurodegenerative diseases and offer promise for early diagnosis and personalized treatment. Through systematic review and meta-analysis, we highlight the need for integrative models that combine multi-modal data and emphasize the importance of model interpretability. Our high-dimensional atlas of T cell diversity aids in revealing tissue-specific cytokine signatures, enriching the understanding of disease progression. By presenting data visually, including graphs, heat maps, and progression charts, we facilitate the comprehension of intricate patterns. This research not only contributes to neurodegenerative disease research but also underscores the significance of predictive analysis in improving patient outcomes. We provide recommendations for future research, paving the way for a more comprehensive understanding of these complex conditions.

Keywords:

NEURODEGENERATIVE DISEASES, PREDICTIVE ANALYSIS, BIG DATA ANALYTICS, ARTIFICIAL INTELLIGENCE, TRANSCRIPTOMIC SIGNATURES, EARLY DIAGNOSIS, PERSONALIZED TREATMENT, MULTI-MODAL DATA INTEGRATION