Review



maldi-tof spectra and ast data  (Mendeley Ltd)

 
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    Mendeley Ltd maldi-tof spectra and ast data
    Schematic workflow of the study. Spectra preprocessing: raw spectra were preprocessed with a five-step pipeline . To generate the benchmarking feature sets, we conducted fixed-length binning (3 Da, 20 Da, and 30 Da) of preprocessed spectra, resulting in feature vectors of 6,000, 900, and 600 dimensions, respectively. Dynamic binning: we determined the mean number of intensity peaks in 500 Da intervals from 2,000 to 20,000 Da. The bin widths used for each region (ranging from 1 Da to 100 Da) are inversely proportional to the corresponding mean peak count of that region. Latent representation learning: a vision transformer model was trained to differentiate high-risk bacterial pathogens . The final hidden layer of the model was used as embedding to extract additional features from <t>the</t> <t>MALDI-TOF</t> spectra. Model development: we used 5-fold cross-validation with 80%–20% training–testing split to train models. Performance evaluation: AUROC was the key performance metric. Model interpretation: the Shapley additive explanations (SHAP) algorithm was used to identify feature bins with high importance . We then analyzed the regions of interest to interpret models and identify potential biological AMR determinants. Abbreviations: MALDI-TOF MS, matrix-assisted laser desorption/ionization–time of flight mass spectrometry; <t>AST,</t> antimicrobial susceptibility testing; BMD, broth microdilution; MIC, minimum inhibitory concentration; m / z , mass-to-charge ratio.
    Maldi Tof Spectra And Ast Data, supplied by Mendeley Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/maldi-tof spectra and ast data/product/Mendeley Ltd
    Average 90 stars, based on 1 article reviews
    maldi-tof spectra and ast data - by Bioz Stars, 2026-06
    90/100 stars

    Images

    1) Product Images from "Predicting Pseudomonas aeruginosa drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra"

    Article Title: Predicting Pseudomonas aeruginosa drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra

    Journal: mSystems

    doi: 10.1128/msystems.00789-24

    Schematic workflow of the study. Spectra preprocessing: raw spectra were preprocessed with a five-step pipeline . To generate the benchmarking feature sets, we conducted fixed-length binning (3 Da, 20 Da, and 30 Da) of preprocessed spectra, resulting in feature vectors of 6,000, 900, and 600 dimensions, respectively. Dynamic binning: we determined the mean number of intensity peaks in 500 Da intervals from 2,000 to 20,000 Da. The bin widths used for each region (ranging from 1 Da to 100 Da) are inversely proportional to the corresponding mean peak count of that region. Latent representation learning: a vision transformer model was trained to differentiate high-risk bacterial pathogens . The final hidden layer of the model was used as embedding to extract additional features from the MALDI-TOF spectra. Model development: we used 5-fold cross-validation with 80%–20% training–testing split to train models. Performance evaluation: AUROC was the key performance metric. Model interpretation: the Shapley additive explanations (SHAP) algorithm was used to identify feature bins with high importance . We then analyzed the regions of interest to interpret models and identify potential biological AMR determinants. Abbreviations: MALDI-TOF MS, matrix-assisted laser desorption/ionization–time of flight mass spectrometry; AST, antimicrobial susceptibility testing; BMD, broth microdilution; MIC, minimum inhibitory concentration; m / z , mass-to-charge ratio.
    Figure Legend Snippet: Schematic workflow of the study. Spectra preprocessing: raw spectra were preprocessed with a five-step pipeline . To generate the benchmarking feature sets, we conducted fixed-length binning (3 Da, 20 Da, and 30 Da) of preprocessed spectra, resulting in feature vectors of 6,000, 900, and 600 dimensions, respectively. Dynamic binning: we determined the mean number of intensity peaks in 500 Da intervals from 2,000 to 20,000 Da. The bin widths used for each region (ranging from 1 Da to 100 Da) are inversely proportional to the corresponding mean peak count of that region. Latent representation learning: a vision transformer model was trained to differentiate high-risk bacterial pathogens . The final hidden layer of the model was used as embedding to extract additional features from the MALDI-TOF spectra. Model development: we used 5-fold cross-validation with 80%–20% training–testing split to train models. Performance evaluation: AUROC was the key performance metric. Model interpretation: the Shapley additive explanations (SHAP) algorithm was used to identify feature bins with high importance . We then analyzed the regions of interest to interpret models and identify potential biological AMR determinants. Abbreviations: MALDI-TOF MS, matrix-assisted laser desorption/ionization–time of flight mass spectrometry; AST, antimicrobial susceptibility testing; BMD, broth microdilution; MIC, minimum inhibitory concentration; m / z , mass-to-charge ratio.

    Techniques Used: Biomarker Discovery, Mass Spectrometry, Concentration Assay



    Similar Products

    maldi-tof spectra and ast data  (Mendeley Ltd)
    90
    Mendeley Ltd maldi-tof spectra and ast data
    Schematic workflow of the study. Spectra preprocessing: raw spectra were preprocessed with a five-step pipeline . To generate the benchmarking feature sets, we conducted fixed-length binning (3 Da, 20 Da, and 30 Da) of preprocessed spectra, resulting in feature vectors of 6,000, 900, and 600 dimensions, respectively. Dynamic binning: we determined the mean number of intensity peaks in 500 Da intervals from 2,000 to 20,000 Da. The bin widths used for each region (ranging from 1 Da to 100 Da) are inversely proportional to the corresponding mean peak count of that region. Latent representation learning: a vision transformer model was trained to differentiate high-risk bacterial pathogens . The final hidden layer of the model was used as embedding to extract additional features from <t>the</t> <t>MALDI-TOF</t> spectra. Model development: we used 5-fold cross-validation with 80%–20% training–testing split to train models. Performance evaluation: AUROC was the key performance metric. Model interpretation: the Shapley additive explanations (SHAP) algorithm was used to identify feature bins with high importance . We then analyzed the regions of interest to interpret models and identify potential biological AMR determinants. Abbreviations: MALDI-TOF MS, matrix-assisted laser desorption/ionization–time of flight mass spectrometry; <t>AST,</t> antimicrobial susceptibility testing; BMD, broth microdilution; MIC, minimum inhibitory concentration; m / z , mass-to-charge ratio.
    Maldi Tof Spectra And Ast Data, supplied by Mendeley Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/maldi-tof spectra and ast data/product/Mendeley Ltd
    Average 90 stars, based on 1 article reviews
    maldi-tof spectra and ast data - by Bioz Stars, 2026-06
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Schematic workflow of the study. Spectra preprocessing: raw spectra were preprocessed with a five-step pipeline . To generate the benchmarking feature sets, we conducted fixed-length binning (3 Da, 20 Da, and 30 Da) of preprocessed spectra, resulting in feature vectors of 6,000, 900, and 600 dimensions, respectively. Dynamic binning: we determined the mean number of intensity peaks in 500 Da intervals from 2,000 to 20,000 Da. The bin widths used for each region (ranging from 1 Da to 100 Da) are inversely proportional to the corresponding mean peak count of that region. Latent representation learning: a vision transformer model was trained to differentiate high-risk bacterial pathogens . The final hidden layer of the model was used as embedding to extract additional features from the MALDI-TOF spectra. Model development: we used 5-fold cross-validation with 80%–20% training–testing split to train models. Performance evaluation: AUROC was the key performance metric. Model interpretation: the Shapley additive explanations (SHAP) algorithm was used to identify feature bins with high importance . We then analyzed the regions of interest to interpret models and identify potential biological AMR determinants. Abbreviations: MALDI-TOF MS, matrix-assisted laser desorption/ionization–time of flight mass spectrometry; AST, antimicrobial susceptibility testing; BMD, broth microdilution; MIC, minimum inhibitory concentration; m / z , mass-to-charge ratio.

    Journal: mSystems

    Article Title: Predicting Pseudomonas aeruginosa drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra

    doi: 10.1128/msystems.00789-24

    Figure Lengend Snippet: Schematic workflow of the study. Spectra preprocessing: raw spectra were preprocessed with a five-step pipeline . To generate the benchmarking feature sets, we conducted fixed-length binning (3 Da, 20 Da, and 30 Da) of preprocessed spectra, resulting in feature vectors of 6,000, 900, and 600 dimensions, respectively. Dynamic binning: we determined the mean number of intensity peaks in 500 Da intervals from 2,000 to 20,000 Da. The bin widths used for each region (ranging from 1 Da to 100 Da) are inversely proportional to the corresponding mean peak count of that region. Latent representation learning: a vision transformer model was trained to differentiate high-risk bacterial pathogens . The final hidden layer of the model was used as embedding to extract additional features from the MALDI-TOF spectra. Model development: we used 5-fold cross-validation with 80%–20% training–testing split to train models. Performance evaluation: AUROC was the key performance metric. Model interpretation: the Shapley additive explanations (SHAP) algorithm was used to identify feature bins with high importance . We then analyzed the regions of interest to interpret models and identify potential biological AMR determinants. Abbreviations: MALDI-TOF MS, matrix-assisted laser desorption/ionization–time of flight mass spectrometry; AST, antimicrobial susceptibility testing; BMD, broth microdilution; MIC, minimum inhibitory concentration; m / z , mass-to-charge ratio.

    Article Snippet: The data sets and computer code produced in this study are available in the following databases: MALDI-TOF spectra and AST data, Mendeley (DOI: 10.17632/tpcznh9968.1 10.17632/tpcznh9968.1 ) ( ); source code, GitHub ( https://github.com/andyvng/pae-amr-maldi ) ( ).

    Techniques: Biomarker Discovery, Mass Spectrometry, Concentration Assay