Alzheimer’s Disease (AD) remains a formidable challenge in neurodegenerative research, necessitating innovative approaches to uncover novel therapeutic strategies. This study presents a graph-based approach to integrate large-scale drug and protein data, aiming to identify potential drug repurposing candidates for AD. Our methodology constructs a comprehensive graph incorporating protein-protein interactions and drug-protein relations, providing a multifaceted view of the intricate relationships within the biological and pharmacological landscape. By leveraging this graph, we conduct an in-depth analysis to explore various drug repurposing possibilities, focusing on the alignment of AD-related single-cell transcriptomic data. Our approach enables the identification of promising drug candidates by examining the connectivity and interaction patterns within the graph, revealing potential therapeutic targets and drug synergies that may be beneficial for AD treatment. The integration of diverse data types allows for a more holistic understanding of the underlying molecular mechanisms and drug interactions. Through this graph-based analysis, we uncover several promising drug repurposing candidates, providing a foundation for further experimental validation and clinical investigation. This study underscores the potential of leveraging large-scale data and graph-based methodologies in drug repurposing efforts for neurodegenerative diseases, contributing to the advancement of therapeutic research in AD. Our findings illuminate the importance of integrative data analysis in biomedical research, paving the way for the development of more effective and targeted therapeutic interventions for AD.

Predicting the three-dimensional structure of proteins directly from their sequence of amino acids remains a challenge in biomedical research. Protein functionality depends not only on its sequence but also on the precise folding that occurs during the process of developing their tertiary structure. Misfolded proteins may lead to the generation of entities that are inherently toxic to the organism, such as the formation of amyloid fibrils in the context of Alzheimer’s disease. Herein, the structural conformation of specific missense mutations in proteins involved in Alzheimer’s disease was performed through computational analysis and prediction of the binding mode and multiplicity of them was further assessed. Our findings reveal direct sequence-to-structure motifs from single polypeptides and the received domains with the proper fold. Nevertheless, the positions with the particular deviations are most commonly accompanied by limited downward spikes in pLDDT value, suggesting lower prediction confidence and potential disorder.

One challenge facing Systems Biology is the conversion of vast amounts of data into systematic and organized knowledge through automated processes. Improvements in experimental technologies have created an enormous pool of heterogeneous omics data such as genomics, proteomics and metabolomics. Exporting insights of these datasets can lead to important discoveries for complex pathologies such as age-related neurodegenerative disorders and specifically Parkinson's disease (PD). However, such data are characterized by huge dimensionality which increases their complexity for various data analyses as well for data mining processes such as clustering and classification. In this perspective, we implemented state-of-the-art Dimensionality Reduction Techniques for Visualizing Omics High-Dimensional Parkinson’s Disease Data. Our study highlights the cutting-edge dimensionality reduction techniques for 2D data visualization and their contribution the deeper interpretation of PD data. Approaches in this direction can provide a deeper understanding of biological dynamics and enable integrative multilayered diagnostic assessment of complex disorders such as neurodegenerative diseases.

Diagnosis of Autism Spectrum Disorder (ASD) is a complex task that typically relies on the expertise of the clinician due to the lack of specific quantitative biomarkers. As a consequence, automatic categorization of an individual within the ASD taxonomy poses many challenges, usually with controversial results. The implementation of Machine Learning approaches as a diagnostic tool for ASD classification is rapidly growing in the field of neuroscience, holding the potential to enhance discrimination validity among ASD and Typically Developed (TD) individuals, while providing indications in regard to ASD differentiating factors. In this study, various feature selection and classification techniques were employed in order to successfully discern between ASD and TD, using data from large resting-state functional Magnetic Resonance Imaging (rs-fMRI) database. Moreover, we adopt novel features, namely the Haralick texture features and the Kullback-Leibler divergence, combined with already established ones (i.e. static Functional Connectivity and demographics), assessing the most informative global attributes. Our framework succeeded in the identification of a small number of discriminative features, leading to high performance relative to previous works with optimal classification accuracy of 0.725.

We are in the big data era which has affected several domains including biomedicine and healthcare. This revolution driven by the explosion of biomedical data offers the potential for better understanding of biology and human diseases. An illustrative example is the emerging single-cell sequencing technologies, which isolate and measure each cell individually, taking a step beyond the traditional techniques where consider their measurements from a bulk of cell. Although big single-cell RNA sequencing (scRNA-seq) data promises valuable insights into the cellular level, their volume poses several challenges related to the ultra-high dimensionality. Furthermore, to further elucidate the potential of these data, more insight into gene regulatory networks (GRN) is required. Network-based approaches can tackle part of the inherent complexity of human diseases, however, the challenges related to the ultra-high dimensionality are increased. Towards this direction, we propose the NIRP, an algorithm that copes with the high dimensionality of scRNA-data using a workflow based on fast multiple random projections and a radius-based nearest neighbors search. NIRP infers a gene regulatory network (GRN) from big scRNA-seq data by transforming the original data space to a lower dimensions space and capturing the similarities among gene expressions. The network is further analyzed using a random walk approach in order to achieve dense subgraphs, active to the case under study. The performance of NIRP is evaluated in a real single-cell experimental study among three well-established GRN tools. Our results make NIRP a reliable tool, able to handle big single-cell data with ultra-high dimensionality and complexity. he main advantage of this method is that it is not affected by the volume, as much as it increases, since it transforms the data space to a specific low dimensional space.