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Correction: A machine learning approach-based array sensor for rapidly predicting the mechanisms of action of antibacterial compounds
Zhijun
Li
a,
Kun
Jin
a,
Hong
Chen
bc,
Liyuan
Zhang
*de,
Guitao
Zhang
a,
Yizhou
Jiang
a,
Haixia
Zou
a,
Wentao
Wang
a,
Guangpei
Qi
a and
Xiangmeng
Qu
*a
aKey Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province and School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518107, China. E-mail: quxm5@mail.sysu.edu.cn
bPen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361005, China
cJiujiang Research Institute of Xiamen University, Jiujiang 332000, China
dHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, c, MA 02138, USA. E-mail: liyuanzhang@seas.harvard.edu
eSchool of Petroleum Engineering, State Key Laboratory of Heavy Oil Processing, China University of Petroleum (East China), Qingdao, 266580, China
First published on 17th March 2022
Abstract
Correction for ‘A machine learning approach-based array sensor for rapidly predicting the mechanisms of action of antibacterial compounds’ by Zhijun Li et al., Nanoscale, 2022, 14, 3087–3096, DOI: 10.1039/D1NR07452K.
The authors regret that in the original article the panels of Fig. 4 were presented in the incorrect order. The correct version of Fig. 4, along with the original caption, is as displayed herein.
 | ||
| Fig. 4 Validation of a machine learning approach-based array sensor. (a) Heat map of the fluorescence response signal of antibacterial compounds in a validation cohort (n = 24, MRSA induced by four antibacterial compounds (each sensor element sensing data of different antibacterial compounds are normalized separately)). The MoA of each compound is present under each column. (b) ROC curves of the machine learning approach-based array sensor with different sensing element combinations (from I to IX). (c) Area under the ROC curve (AUC), which represents the recognition performance. (d) Confusion matrices built from the prediction in the validation (a) testing cohorts. (e) Overall accuracy of MoA prediction by different algorithms. | ||
The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers.
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