Nature Communications (Nov 2022)

Systematic analysis and prediction of genes associated with monogenic disorders on human chromosome X

  • Elsa Leitão,
  • Christopher Schröder,
  • Ilaria Parenti,
  • Carine Dalle,
  • Agnès Rastetter,
  • Theresa Kühnel,
  • Alma Kuechler,
  • Sabine Kaya,
  • Bénédicte Gérard,
  • Elise Schaefer,
  • Caroline Nava,
  • Nathalie Drouot,
  • Camille Engel,
  • Juliette Piard,
  • Bénédicte Duban-Bedu,
  • Laurent Villard,
  • Alexander P. A. Stegmann,
  • Els K. Vanhoutte,
  • Job A. J. Verdonschot,
  • Frank J. Kaiser,
  • Frédéric Tran Mau-Them,
  • Marcello Scala,
  • Pasquale Striano,
  • Suzanna G. M. Frints,
  • Emanuela Argilli,
  • Elliott H. Sherr,
  • Fikret Elder,
  • Julien Buratti,
  • Boris Keren,
  • Cyril Mignot,
  • Delphine Héron,
  • Jean-Louis Mandel,
  • Jozef Gecz,
  • Vera M. Kalscheuer,
  • Bernhard Horsthemke,
  • Amélie Piton,
  • Christel Depienne

DOI
https://doi.org/10.1038/s41467-022-34264-y
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 17

Abstract

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Discovering disease genes on the X chromosome can be particularly challenging. Here, the authors use features of known disease genes and machine learning to predict genes that remain to be associated with disorders on this chromosome.