Abstract
Computational approach to study of neuronal impairment is rapidly evolving, as experiments and intuition alone could not explain the complexity of brain system. The increase in an overwhelming amount of new data from both theory and computational modeling necessitate the development of databases and tools for analysis, visualization, and interpretation of neuroscience data. To ensure the sustainability of this development, consistent update and training of young professionals are imperative. For this purpose, relevant articles, chapters, and modules are essential to keep abreast of developments. Therefore, this article seeks to outline the biological databases and analytical tools along with their applications. It's envisaged that knowledge along this line would be a "training recipe" for young talents and guide for professionals and researchers in neuroscience.
Highlights
Neuronal impairments or neurological disorders (NDs) continue to attract much research attention owing to both their unknown etiology and the interwoven complexities of their underlying molecular and signaling pathways that often make the identification of practical classifications and therapeutic targets difficult
With an increasing number of large databases, data sharing in neuroscience may gradually become as common and useful as it is in genomics, where the existence of very large bodies of data is leading to increased knowledge as well as products and services linked to the improvement of human health
The increase in synergistic relationship between computational and experimental neuroscience leads to the emergence of more analytical tools and databases
Summary
Neuronal impairments or neurological disorders (NDs) continue to attract much research attention owing to both their unknown etiology and the interwoven complexities of their underlying molecular and signaling pathways that often make the identification of practical classifications and therapeutic targets difficult. With recent advancement in computational methods, investigators are able to employ several databases and tools to demystify these complex networks for the identification of better therapeutic or mechanistic targets [1, 2] These resources are useful for the study of bio-molecular interaction networks, and help in network analysis, visualization and the establishment of relationships at the level of their genes and protein products. The various forms of, edical interaction networks being analyzed include: gene interaction (GI), protein-protein interaction (PPI), protein-DNA interactions, and protein-RNA interactions [3, 4] Computational analysis of these interactions has revolutionized the modern understanding of NDs and provides better prospects for drug discovery. To solve increasingly complex problems, clinicians and research scientists use computational tools, mathematical models and Neuroinformatic databases provided by neuroinformaticians to collaborate, share information and quantitatively support working theories [15]
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