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. 2022 Nov 9;12:19049. doi: 10.1038/s41598-022-23620-z

Author Correction: Discriminant analysis and binary logistic regression enable more accurate prediction of autism spectrum disorder than principal component analysis

Wail M Hassan 1, Abeer Al-Dbass 2, Laila Al-Ayadhi 3,4, Ramesa Shafi Bhat 2, Afaf El-Ansary 4,5,
PMCID: PMC9646825  PMID: 36352027

Correction to: Scientific Reports 10.1038/s41598-022-07829-6, published online 08 March 2022

The original version of this Article contained an error in Figure 4, where an incorrect figure was displayed. The original Figure 4 and accompanying legend appear below.

Figure 4.

Figure 4

Testing the predictive power of five biomarkers using receiver operating characteristic curve. Areas under the curve (AUC) and p values are indicated. Analysis was performed on ASD (n = 40) and healthy (n = 40) volunteers. PC1: first principal component scores computed in principal component analysis. Disc1: first discriminant scores computed in discriminant analysis. PProb predicted probability computed by binary logistic regression, K plasma potassium, Na plasma sodium, LDH plasma lactate dehydrogenase, GST plasma glutamate S-transferase, MRC1 mitochondrial respiratory chain complex I activity, PC1 the first principal component in principal component analysis. Figure was generated using IBM SPSS Statistics for Windows, Version 27.0, IBM Corp., Armonk, New York, https://www.ibm.com.

The original Article has been corrected.


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