{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:47:23Z","timestamp":1760122043208,"version":"build-2065373602"},"reference-count":16,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T00:00:00Z","timestamp":1672790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Following breakthrough achievements in molecular neurosciences, the current decade witnesses a trend toward interdisciplinary and multimodal development. Supplementation of neurosciences with tools from computer science solidifies previous knowledge and sets the ground for new research on \u201cbig data\u201d and new hypothesis-free experimental models. In this Special Issue, we set the focus on informatics-supported interdisciplinary neuroscience accomplishments symmetrically combining wet-lab and clinical routines. Video-tracking and automated mitosis detection in vitro, the macromolecular modeling of kinesin motion, and the unsupervised classification of the brain\u2019s macrophage activation status share a common denominator: they are energized by machine and deep learning. Essential clinical neuroscience questions such as the estimated risk of brain aneurysm rupture and the surgical outcome of facial nerve transplantation are addressed in this issue as well. Precise and rapid evaluation of complex clinical data by deep learning and data mining dives deep to reveal symmetrical and asymmetrical features beyond the abilities of human perception or the limits of linear algebraic modeling. This editorial opts to motivate researchers from the wet lab, computer science, and clinical environments to join forces in reshaping scientific platforms, share and converge high-quality data on public platforms, and use informatics to facilitate interdisciplinary information exchange.<\/jats:p>","DOI":"10.3390\/sym15010153","type":"journal-article","created":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T01:33:30Z","timestamp":1672882410000},"page":"153","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Neuroscience Scaffolded by Informatics: A Raging Interdisciplinary Field"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5810-0483","authenticated-orcid":false,"given":"Ismini E.","family":"Papageorgiou","sequence":"first","affiliation":[{"name":"Institute of Diagnostic and Interventional Radiology, University Hospital of Jena, Am Klinikum 1, 07747 Jena, Germany"},{"name":"Institute of Radiology, Suedharz Hospital Nordhausen, Dr-Robert-Koch Street 39, 99734 Nordhausen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,4]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Hadjidemetriou, S., Hadjisavva, R., Christodoulou, A., Papageorgiou, I., Panayiotou, I., and Skourides, P. (2022). Spatiotemporal Identification of Cell Divisions Using Symmetry Properties in Time-Lapse Phase Contrast Microscopy. Symmetry, 14.","key":"ref_1","DOI":"10.3390\/sym14091802"},{"doi-asserted-by":"crossref","unstructured":"Xie, P. (2021). Molecular Mechanism of Processive Stepping of Kinesin Motors. Symmetry, 13.","key":"ref_2","DOI":"10.3390\/sym13101799"},{"doi-asserted-by":"crossref","unstructured":"Papageorgiou, I., Bittner, D., Psychogios, M.N., and Hadjidemetriou, S. (2021). Brain Immunoinformatics: A Symmetrical Link between Informatics, Wet Lab and the Clinic. Symmetry, 13.","key":"ref_3","DOI":"10.3390\/sym13112168"},{"doi-asserted-by":"crossref","unstructured":"Walther, G., Martin, C., Haase, A., Nestler, U., and Schob, S. (2022). Machine Learning for Rupture Risk Prediction of Intracranial Aneurysms: Challenging the PHASES Score in Geographically Constrained Areas. 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