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Correction : Random-forest model for drug–target interaction prediction via Kullback–Leibler divergence

Journal of Cheminformatics volume 14, Article number: 76 (2022) Cite this article

The Original Article was published on 03 October 2022

Correction: Journal of Cheminformatics (2022) 14:67 https://doi.org/10.1186/s13321-022-00644-1

Following publication of the original article [1], the authors identified a spelling in the title and in the body of the text.

Incorrect: Kullbeck.

Correct: Kullback.

The original article has been corrected.

Reference

  1. Ahn S, Lee S, Kim MH (2022) Random-forest model for drug–target interaction prediction via Kullback–Leibler divergence. J Cheminformatics 14:67. https://doi.org/10.1186/s13321-022-00644-1

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Authors and Affiliations

  1. Gachon Institute of Pharmaceutical Science and Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-Gu, Incheon, Republic of Korea

    Sangjin Ahn, Si Eun Lee & Mi-hyun Kim

  2. Department of Artificial Intelligence, Ajou University, Suwon, 16499, Republic of Korea

    Sangjin Ahn

Authors
  1. Sangjin Ahn
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  2. Si Eun Lee
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  3. Mi-hyun Kim
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Correspondence to Mi-hyun Kim.

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Ahn, S., Lee, S.E. & Kim, Mh. Correction : Random-forest model for drug–target interaction prediction via Kullback–Leibler divergence. J Cheminform 14, 76 (2022). https://doi.org/10.1186/s13321-022-00653-0

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  • DOI: https://doi.org/10.1186/s13321-022-00653-0

Journal of Cheminformatics

ISSN: 1758-2946

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