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  1. ; ; ; ( , Journal of biomolecular NMR)
    Rapid progress in machine learning offers new opportunities for the automated analysis of multidimensional NMR spectra ranging from protein NMR to metabolomics applications. Most recently, it has been demonstrated how deep neural networks (DNN) designed for spectral peak picking are capable of deconvoluting highly crowded NMR spectra rivaling the facilities of human experts. Superior DNN-based peak picking is one of a series of critical steps during NMR spectral processing, analysis, and interpretation where machine learning is expected to have a major impact. In this perspective, we lay out some of the unique strengths as well as challenges of machine learningmore »approaches in this new era of automated NMR spectral analysis. Such a discussion seems timely and should help define common goals for the NMR community, the sharing of software tools and standardization of protocols, and calibrate expectations. It will also help prepare for an NMR future where machine learning and artificial intelligence tools will be common place.« less
    Free, publicly-accessible full text available April 1, 2023
  2. ; ; ; ; ( , IEEE transactions on intelligent transportation systems)
    Free, publicly-accessible full text available February 22, 2023
  3. ; ; ; ( , 2021 IEEE Symposium Series on Computational Intelligence (SSCI))
    Free, publicly-accessible full text available January 24, 2023
  4. ; ; ; ; ( , IEEE ICDM)
    Free, publicly-accessible full text available December 1, 2022
  5. ; ; ; ; ; ; ( , Proceedings of Neural Information Processing Systems (NeurIPS), 2021.)
    Free, publicly-accessible full text available December 1, 2022
  6. ; ; ; ; ; ; ; ( , ACM CIKM)
    Free, publicly-accessible full text available November 1, 2022
  7. ; ; ; ; ( , Nature communications)
    The analysis of nuclear magnetic resonance (NMR) spectra for the comprehensive and unambiguous identification and characterization of peaks is a difficult, but critically important step in all NMR analyses of complex biological molecular systems. Here, we introduce DEEP Picker, a deep neural network (DNN)-based approach for peak picking and spectral deconvolution which semi-automates the analysis of two-dimensional NMR spectra. DEEP Picker includes 8 hidden convolutional layers and was trained on a large number of synthetic spectra of known composition with variable degrees of crowdedness. We show that our method is able to correctly identify overlapping peaks, including ones that aremore »challenging for expert spectroscopists and existing computational methods alike. We demonstrate the utility of DEEP Picker on NMR spectra of folded and intrinsically disordered proteins as well as a complex metabolomics mixture, and show how it provides access to valuable NMR information. DEEP Picker should facilitate the semi-automation and standardization of protocols for better consistency and sharing of results within the scientific community.« less
    Free, publicly-accessible full text available October 1, 2022
  8. ; ; ; ; ; ; ( , Journal of the American Chemical Society)
    Free, publicly-accessible full text available October 1, 2022
  9. ; ; ; ; ; ; ; ; ; ; et al ( , Journal of Physics: Conference Series)
    Abstract Testing the DAMA/LIBRA annual modulation result independently of dark matter particle and halo models has been a challenge for twenty years. Using the same target material, NaI(Tl), is required and presently two experiments, ANAIS-112 and COSINE-100, are running for such a goal. A precise knowledge of the detector response to nuclear recoils is mandatory because this is the most likely channel to find the dark matter signal. The light produced by nuclear recoils is quenched with respect to that produced by electrons by a factor that has to be measured experimentally. However, current quenching factor measurements in NaI(Tl) crystalsmore »disagree within the energy region of interest for dark matter searches. To disentangle whether this discrepancy is due to intrinsic differences in the light response among different NaI(Tl) crystals, or has its origin in unaccounted for systematic effects will be key in the comparison among the different experiments. We present measurements of the quenching factors for five small NaI(Tl) crystals performed in the same experimental setup to control systematics. Quenching factor results are compatible between crystals and no clear dependence with energy is observed from 10 to 80 keVnr.« less
    Free, publicly-accessible full text available December 1, 2022
  10. ; ; ; ; ; ; ; ; ; ; et al ( , Earth-Science Reviews)
    Free, publicly-accessible full text available February 1, 2023