Helix Matrix Transformation Combined With Convolutional Neural Network Algorithm for Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry-Based Bacterial Identification

Jin, Ling; Li, Gaomin; Shao, Hong; Wang, Hong; Yin, Hongrui; Zhou, Hu; Song, Yufei; Chen, Gang

doi:10.3389/fmicb.2020.565434

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…Some research uses deep learning to lter the peaks of interest and ll the feature extraction step just before the classi cation by a machine learning model [34]. Ling et al [35] have a different approach both in the preparation of the spectra and in the representation. As the spectra are one-dimensional data, the authors addressed the issue of transforming the spectrum into a 2D image that would be sent to a 2-dimensional CNN model.…”

Section: Comparison With Other Work Coupling Maldi-tof and Machine Le...mentioning

confidence: 99%

Deep learning model coupled with proteomics to detect yeast epidemic clones across multiple hospitals

Mohammad

Normand

Nabet

et al. 2022

Preprint

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The ongoing worldwide emergence of epidemic azole-resistant Candida parapsilosis clones is a threat to human health. In order to monitor a clonal outbreak which involved 2 hospitals in Paris, we used a technology that most clinical laboratories are equipped with: Matrix-Assisted Laser Desorption-Ionization Time of Flight mass spectrometry (MALDI-TOF MS). This physico-chemical analysis technique was combined with machine learning algorithm to evaluate the possibility of monitoring a multi-center epidemic. A total of 96 isolates of Candida parapsilosis constituted of resistant clones, susceptible clones and other susceptible isolates were cultured on various culture conditions. Spectra were acquired on four different mass spectrometers from three hospitals. We used convolutional neural networks (CNN) to classify the isolates and identified the main parameters that can influence the identification of epidemic clonal isolates. We have shown results up to 94% accuracy in a multi-center context, and between 81 and 91% accuracy when the tested site is not part of the training set, showing that it is possible to differentiate the spectra of a clone and a non-clone presenting a very strong similarity. This study shows that it is possible to monitor a certain type of clone, namely the R2 clones in our case study, using MALDI-TOF coupled to CNN. The experimental conditions that impacted the results should be considered for future work to improve performances for difficult classification tasks like clonal fungal identification.

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Section: Comparison With Other Work Coupling Maldi-tof and Machine Le...mentioning

confidence: 99%

Deep learning model coupled with proteomics to detect yeast epidemic clones across multiple hospitals

Mohammad

Normand

Nabet

et al. 2022

Preprint

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“…Spectral characteristic extraction is very important in the signal preprocessing step ( Fukuhara et al, 2019 ; Galeev et al, 2019 ; Ling et al, 2020 ). Original spectral data usually contain many invalid data points, which will cause a serious performance loss and low accuracy rate.…”

Section: Introductionmentioning

confidence: 99%

Extreme Point Sort Transformation Combined With a Long Short-Term Memory Network Algorithm for the Raman-Based Identification of Therapeutic Monoclonal Antibodies

Jin

Zheng

et al. 2022

Front. Chem.

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Therapeutic monoclonal antibodies (mAbs) are a new generation of protein-based medicines that are usually expensive and thus represent a target for counterfeiters. In the present study, a method based on Raman spectroscopy that combined extreme point sort transformation with a long short-term memory (LSTM) network algorithm was presented for the identification of therapeutic mAbs. A total of 15 therapeutic mAbs were used in this study. An in-house Raman spectrum dataset for model training was created with 1,350 spectra. The characteristic region of the Raman spectrum was reduced in dimension and then transformed through an extreme point sort transformation into a sequence array, which was fitted for the LSTM network. The characteristic array was extracted from the sequence array using a well-trained LSTM network and then compared with standard spectra for identification. To demonstrate whether the present algorithm was better, ThermoFisher OMNIC 8.3 software (Thermo Fisher Scientific Inc., U.S.) with two matching modes was selected for comparison. Finally, the present method was successfully applied to identify 30 samples, including 15 therapeutic mAbs and 15 other injections. The characteristic region was selected from 100 to 1800 cm−1 of the full spectrum. The optimized dimensional values were set from 35 to 53, and the threshold value range was from 0.97 to 0.99 for 15 therapeutic mAbs. The results of the robustness test indicated that the present method had good robustness against spectral peak drift, random noise and fluorescence interference from the measurement. The areas under the curve (AUC) values of the present method that were analysed on the full spectrum and analysed on the characteristic region by the OMNIC 8.3 software’s built-in method were 1.000, 0.678, and 0.613, respectively. The similarity scores for 15 therapeutic mAbs using OMNIC 8.3 software in all groups compared with that of the relative present algorithm group had extremely remarkable differences (p < 0.001). The results suggested that the extreme point sort transformation combined with the LSTM network algorithm enabled the characteristic extraction of the therapeutic mAb Raman spectrum. The present method is a proposed solution to rapidly identify therapeutic mAbs.

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Helix Matrix Transformation Combined With Convolutional Neural Network Algorithm for Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry-Based Bacterial Identification

Cited by 2 publications

References 27 publications

Deep learning model coupled with proteomics to detect yeast epidemic clones across multiple hospitals

Deep learning model coupled with proteomics to detect yeast epidemic clones across multiple hospitals

Extreme Point Sort Transformation Combined With a Long Short-Term Memory Network Algorithm for the Raman-Based Identification of Therapeutic Monoclonal Antibodies

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