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Identification of genetic markers for cortical areas using a Random Forest classification routine and the Allen Mouse Brain Atlas

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  • Natalie Weed
  • Trygve Bakken
  • Nile Graddis
  • Nathan Gouwens
  • Daniel Millman
  • Michael Hawrylycz
  • Jack Waters

Abstract

The mammalian neocortex is subdivided into a series of cortical areas that are functionally and anatomically distinct and are often distinguished in brain sections using histochemical stains and other markers of protein expression. We searched the Allen Mouse Brain Atlas, a database of gene expression, for novel markers of cortical areas. To screen for genes that change expression at area borders, we employed a random forest algorithm and binary region classification. Novel genetic markers were identified for 19 of 39 areas and provide code that quickly and efficiently searches the Allen Mouse Brain Atlas. Our results demonstrate the utility of the random forest algorithm for cortical area classification and we provide code that may be used to facilitate the identification of genetic markers of cortical and subcortical structures and perhaps changes in gene expression in disease states.

Suggested Citation

  • Natalie Weed & Trygve Bakken & Nile Graddis & Nathan Gouwens & Daniel Millman & Michael Hawrylycz & Jack Waters, 2019. "Identification of genetic markers for cortical areas using a Random Forest classification routine and the Allen Mouse Brain Atlas," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-13, September.
    • Handle: RePEc:plo:pone00:0212898
    DOI: 10.1371/journal.pone.0212898
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    1. Matthew F. Glasser & Timothy S. Coalson & Emma C. Robinson & Carl D. Hacker & John Harwell & Essa Yacoub & Kamil Ugurbil & Jesper Andersson & Christian F. Beckmann & Mark Jenkinson & Stephen M. Smith , 2016. "A multi-modal parcellation of human cerebral cortex," Nature, Nature, vol. 536(7615), pages 171-178, August.
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    1. Nabeel Al-Milli & Amjad Hudaib & Nadim Obeid, 2021. "Population Diversity Control of Genetic Algorithm Using a Novel Injection Method for Bankruptcy Prediction Problem," Mathematics, MDPI, vol. 9(8), pages 1-18, April.

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