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| Verfasst von: | Costa, Marta [VerfasserIn]  |
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| | Manton, James D. [VerfasserIn] |
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| | Ostrovsky, Aaron [VerfasserIn] |
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| | Prohaska, Steffen [VerfasserIn] |
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| | Jefferis, Gregory S. X. E. [VerfasserIn] |
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| Titel: | NBLAST |
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| Titelzusatz: | rapid, sensitive comparison of neuronal structure and construction of neuron family databases |
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| Verf.angabe: | Marta Costa, James D. Manton, Aaron D. Ostrovsky, Steffen Prohaska, Gregory S.X.E. Jefferis |
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| E-Jahr: | 2016 |
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| Jahr: | June 30, 2016 |
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| Umfang: | 19 S. |
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| Fussnoten: | Gesehen am 05.05.2020 |
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| Titel Quelle: | Enthalten in: Neuron |
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| Ort Quelle: | [Cambridge, Mass.] : Cell Press, 1988 |
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| Jahr Quelle: | 2016 |
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| Band/Heft Quelle: | 91(2016), 2, Seite 293-311 |
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| ISSN Quelle: | 1097-4199 |
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| Abstract: | Summary Neural circuit mapping is generating datasets of tens of thousands of labeled neurons. New computational tools are needed to search and organize these data. We present NBLAST, a sensitive and rapid algorithm, for measuring pairwise neuronal similarity. NBLAST considers both position and local geometry, decomposing neurons into short segments; matched segments are scored using a probabilistic scoring matrix defined by statistics of matches and non-matches. We validated NBLAST on a published dataset of 16,129 single Drosophila neurons. NBLAST can distinguish neuronal types down to the finest level (single identified neurons) without a priori information. Cluster analysis of extensively studied neuronal classes identified new types and unreported topographical features. Fully automated clustering organized the validation dataset into 1,052 clusters, many of which map onto previously described neuronal types. NBLAST supports additional query types, including searching neurons against transgene expression patterns. Finally, we show that NBLAST is effective with data from other invertebrates and zebrafish. Video Abstract |
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| DOI: | doi:10.1016/j.neuron.2016.06.012 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext ; Verlag: https://doi.org/10.1016/j.neuron.2016.06.012 |
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| | Volltext: https://www.cell.com/neuron/abstract/S0896-6273(16)30265-3 |
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| | DOI: https://doi.org/10.1016/j.neuron.2016.06.012 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| K10plus-PPN: | 1697207294 |
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| Verknüpfungen: | → Zeitschrift |
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