首页|La Trobe University Reports Findings in Machine Learning (A Machine Learning-Dri ven Comparison of Ion Images Obtained by MALDI and MALDI-2 Mass Spectrometry Ima ging)
La Trobe University Reports Findings in Machine Learning (A Machine Learning-Dri ven Comparison of Ion Images Obtained by MALDI and MALDI-2 Mass Spectrometry Ima ging)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating in Bundoora, Aust ralia, by NewsRx journalists, research stated, "Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) enables label-free imaging of biomolecules in biological tissues. However, many species remain undetected due to their poor ionization efficiencies." The news reporters obtained a quote from the research from La Trobe University, "MALDI-2 (laserinduced post-ionization) is the most widely used post-ionization method for improving analyte ionization efficiencies. Mass spectra acquired usi ng MALDI-2 constitute a combination of ions generated by both MALDI and MALDI-2 processes. Until now, no studies have focused on a detailed comparison between t he ion images (as opposed to the generated values) produced by MALDI and MALDI-2 for mass spectrometry imaging (MSI) experiments. Herein, we investigated the io n images produced by both MALDI and MALDI-2 on the same tissue section using cor relation analysis (to explore similarities in ion images for ions common to both MALDI and MALDI-2) and a deep learning approach. For the latter, we used an ana lytical workflow based on the Xception convolutional neural network, which was o riginally trained for human-like natural image classification but which we adapt ed to elucidate similarities and differences in ion images obtained using the tw o MSI techniques. Correlation analysis demonstrated that common ions yielded sim ilar spatial distributions with low-correlation species explained by either poor signal intensity in MALDI or the generation of additional unresolved signals us ing MALDI-2. Using the Xception-based method, we identified many regions in the t-SNE space of spatially similar ion images containing MALDI and MALDI-2-related signals. More notably, the method revealed distinct regions containing only MAL DI-2 ion images with unique spatial distributions that were not observed using M ALDI."
BundooraAustraliaAustralia and New Z ealandCorrelation AnalysisCyborgsEmerging TechnologiesMachine Learning
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