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Modeling the course of amyotrophic lateral sclerosis

Nature Biotechnology volume 33, pages 45–47 (2015)Cite this article

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An Erratum to this article was published on 12 May 2015

This article has been updated

Large-scale computational analysis of patient data leads to better models of disease progression.

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disorder that typically strikes in the prime of life and always leads to untimely death. But progression of the disease in individual patients varies widely and unpredictably, posing a substantial challenge to the design, execution and interpretation of ALS clinical trials. In this issue, Küffner et al.1 report the results of a crowdsourcing challenge aimed at developing computational models that can predict how the disease will progress after diagnosis. The winning algorithms not only beat a baseline model provided by the challenge organizers but also outperformed a group of expert ALS clinicians. The results have potentially important implications for clinical trials as they imply that trials can be designed with ∼20% fewer patients than is done currently.

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Figure 1: ALS disease progression is highly variable among patients.
Figure 2: Design of the DREAM challenge to identify algorithms that predict ALS disease progression1.

Change history

  • 30 April 2015

    In the version of this article initially published, the number of patients said to be included in the PRO-ACT database was given as 1,822. There are 8,635 patients in the database to date, of which 1,822 were included in the crowdsourcing challenge. The error has been corrected in the HTML and PDF versions of the article.

References

  1. Küffner, R. et al. Nat. Biotechnol. 33, 51–57 (2015).

    Article  Google Scholar 

  2. Glass, J.D. Exp. Neurol. 233, 112–117 (2012).

    Article  Google Scholar 

  3. Abramson, V. & Arteaga, C.L. Clin. Cancer Res. 17, 952–958 (2011).

    Article  CAS  Google Scholar 

  4. Atassi, N. et al. Neurology 83, 1719–1725 (2014).

    Article  CAS  Google Scholar 

  5. Bedlack, R.S., Pastula, D.M., Welsh, E., Pulley, D. & Cudkowicz, M.E. Amyotroph. Lateral Scler. 9, 257–265 (2008).

    Article  Google Scholar 

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Authors and Affiliations

  1. Christina Fournier and Jonathan D. Glass are at Emory University School of Medicine, Atlanta, Georgia, USA.,

    Christina Fournier & Jonathan D Glass

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  1. Christina Fournier
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  2. Jonathan D Glass
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Correspondence to Jonathan D Glass.

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The authors declare no competing financial interests.

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Fournier, C., Glass, J. Modeling the course of amyotrophic lateral sclerosis. Nat Biotechnol 33, 45–47 (2015). https://doi.org/10.1038/nbt.3118

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