Journal of chemical information and modeling
Apr 13, 2020
Fast and accurate prediction of NMR spectra enables automatic structure validation and elucidation of molecules on a large scale. In this Article, we propose an improved method of learning from an NMR database to predict the chemical shifts of NMR-ac...
PURPOSE: The aim of this study was to develop a method for metabolite quantification with simultaneous measurement uncertainty estimation in deep learning-based proton magnetic resonance spectroscopy ( H-MRS).
Journal of the American Chemical Society
Feb 21, 2020
This report describes the first application of the novel NMR-based machine learning tool "Small Molecule Accurate Recognition Technology" (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural produc...
PURPOSE: To explore the applicability of convolutional neural networks (CNNs) in the reconstruction of spectra from truncated FIDs (tFIDs) in H-MRS, which can be valuable in situations in which data sampling is highly limited, such as spectroscopic ...
BMC medical informatics and decision making
Dec 12, 2019
BACKGROUND: The automatic segmentation of kidneys in medical images is not a trivial task when the subjects undergoing the medical examination are affected by Autosomal Dominant Polycystic Kidney Disease (ADPKD). Several works dealing with the segmen...
Protein Secondary Structure prediction has been a central topic of research in Bioinformatics for decades. In spite of this, even the most sophisticated ab initio SS predictors are not able to reach the theoretical limit of three-state prediction acc...
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Aug 12, 2019
BACKGROUND: The heart's energy demand per gram of tissue is the body's highest and creatine kinase (CK) metabolism, its primary energy reserve, is compromised in common heart diseases. Here, neural-network analysis is used to test whether noninvasive...
BACKGROUND: The variation in articular cartilage thickness (ACT) in healthy knees is difficult to quantify and therefore poorly documented. Our aims are to (1) define how machine learning (ML) algorithms can automate the segmentation and measurement ...
Journal of magnetic resonance (San Diego, Calif. : 1997)
Jul 16, 2019
Machine learning has been used in NMR in for decades, but recent developments signal explosive growth is on the horizon. An obstacle to the application of machine learning in NMR is the relative paucity of available training data, despite the existen...
Chemical shifts (CS) are determined from NMR experiments and represent the resonance frequency of the spin of atoms in a magnetic field. They contain a mixture of information, encompassing the in-solution conformations a protein adopts, as well as th...
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