Biological databases and tools for neurological disorders

Abstract

Computational approaches to study of neuronal impairment is rapidly evolving, as experiments and intuition alone do not explain the complexity of the brain system. An overwhelming increase in the 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 is imperative. For this purpose, relevant articles, chapters, and modules are essential to keep abreast of developments. This review seeks to outline the biological databases and an-alytical tools along with their applications. It is envisaged that such knowledge could provide a ''training recipe'' for young scientists and a guide for professionals and researchers in neuroscience.

Natural product-based pharmacological studies for neurological disorders

“…Neuroinformatics is the study of the neurological system via the development of databases and tools that aims to design and manage web-accessible databases of experimental and computational data and novel software tools that are necessary for understanding the nervous system in diseased and healthy states ( Pu and Li, 2018 ; Usman et al, 2022 ). Brain imaging using positron emission tomography ( Kaswan et al, 2021 ; Ruiz-Olazar et al, 2021 ), functional magnetic resonance imaging ( Stefanovski et al, 2021 ; Li et al, 2022 ), electroencephalography ( Wojcik et al, 2018 ; Shirbandi et al, 2021 ), magnetoencephalography ( Gorina-Careta et al, 2021 ), and other methods; several electrophysiological recording methods; and clinical neurological data are examples of neuroinformatics ( Sharma et al, 2019 ).…”

Unsupervised model for structure segmentation applied to brain computed tomography

“…The high prevalence of neurological diseases and the growing number of neuroimaging studies stored in repositories [ 4 , 5 ] emphasize the importance of using artificial intelligence (AI) to develop models that can assist physicians in early diagnosis, thus improving patient care [ 6 , 7 ]. By analyzing vast amounts of data, AI can identify intricate and subtle patterns that humans cannot observe.…”