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. 2025 Jan 17;18:5. doi: 10.1186/s13040-025-00423-2

Correction: Predictive modeling of ALS progression: an XGBoost approach using clinical features

Richa Gupta 1,✉, Mansi Bhandari 1, Anhad Grover 1, Taher Al-shehari 2, Mohammed Kadrie 2, Taha Alfakih 3, Hussain Alsalman 4
PMCID: PMC11740421  PMID: 39825381

Correction: BioData Mining 17, 54 (2024)

10.1186/s13040-024-00399-5

Following publication of the original article [1], the authors identified a production error in affiliation 1.

The incorrect affiliation 1 is:

Department of Computer Science, King Saud University, Riyadh, Saudi Arabia.

The correct affiliation 1 is:

Department of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, Delhi, India.

The original article [1] has been corrected.

Footnotes

The online version of the original article can be found at 10.1186/s13040-024-00399-5.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Reference

  • 1.Gupta R, Bhandari M, Grover A, et al. Predictive modeling of ALS progression: an XGBoost approach using clinical features. BioData Min. 2024;17:54. 10.1186/s13040-024-00399-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

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