Machine learning meets mass spectrometry: a focused perspective

• URL: http://arxiv.org/abs/2407.00117v1
• Date: Thu, 27 Jun 2024 14:18:23 GMT
• Title: Machine learning meets mass spectrometry: a focused perspective
• Authors: Daniil A. Boiko, Valentine P. Ananikov,
• Abstract summary: Mass spectrometry is a widely used method to study molecules and processes in medicine, life sciences, chemistry, and industrial product quality control, among many other applications. One of the main features of some mass spectrometry techniques is the extensive level of characterization and a large amount of generated data per measurement. With the development of machine learning methods, the opportunity arises to unlock the potential of these data, enabling previously inaccessible discoveries.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Mass spectrometry is a widely used method to study molecules and processes in medicine, life sciences, chemistry, catalysis, and industrial product quality control, among many other applications. One of the main features of some mass spectrometry techniques is the extensive level of characterization (especially when coupled with chromatography and ion mobility methods, or a part of tandem mass spectrometry experiment) and a large amount of generated data per measurement. Terabyte scales can be easily reached with mass spectrometry studies. Consequently, mass spectrometry has faced the challenge of a high level of data disappearance. Researchers often neglect and then altogether lose access to the rich information mass spectrometry experiments could provide. With the development of machine learning methods, the opportunity arises to unlock the potential of these data, enabling previously inaccessible discoveries. The present perspective highlights reevaluation of mass spectrometry data analysis in the new generation of methods and describes significant challenges in the field, particularly related to problems involving the use of electrospray ionization. We argue that further applications of machine learning raise new requirements for instrumentation (increasing throughput and information density, decreasing pricing, and making more automation-friendly software), and once met, the field may experience significant transformation.

Related papers

• Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning [79.75718786477638]
  We exploit the specialty of molecular tasks that there are physical laws connecting them, and design consistency training approaches. We demonstrate that the more accurate energy data can improve the accuracy of structure prediction. We also find that consistency training can directly leverage force and off-equilibrium structure data to improve structure prediction.
  arXiv  Detail & Related papers  (2024-10-14T03:11:33Z)
• Deep Learning Domain Adaptation to Understand Physico-Chemical Processes from Fluorescence Spectroscopy Small Datasets: Application to Ageing of Olive Oil [4.14360329494344]
  Fluorescence spectroscopy is a fundamental tool in life sciences and chemistry, widely used for applications such as environmental monitoring, food quality control, and biomedical diagnostics. Analysis of spectroscopic data with deep learning, in particular of fluorescence excitation-emission matrices (EEMs), presents significant challenges due to the typically small and sparse datasets available. This study proposes a new approach that exploits domain adaptation with pretrained vision models, alongside a novel interpretability algorithm to address these challenges.
  arXiv  Detail & Related papers  (2024-06-14T13:41:21Z)
• Analyze Mass Spectrometry data with Artificial Intelligence to assist the understanding of past habitability of Mars and provide insights for future missions [0.0]
  This paper presents an application of artificial intelligence on mass spectrometry data for detecting habitability potential of ancient Mars. Although data was collected for planet Mars the same approach can be replicated for any terrestrial object of our solar system.
  arXiv  Detail & Related papers  (2023-10-18T11:14:46Z)
• Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials [66.14337835284628]
  We propose a platform, coined Geom3D, which enables benchmarking the effectiveness of geometric strategies. Geom3D contains 16 advanced symmetry-informed geometric representation models and 14 geometric pretraining methods over 46 diverse datasets.
  arXiv  Detail & Related papers  (2023-06-15T05:37:25Z)
• De-novo Identification of Small Molecules from Their GC-EI-MS Spectra [0.0]
  Machine learning based emphde-novo methods, which derive molecular structure directly from its mass spectrum gained attention recently. We present anovel method in this family, addressing aspecific usecase of GC-EI-MS spectra, which is particularly hard due to lack of additional information from the first stage of MS/MS experiments.
  arXiv  Detail & Related papers  (2023-04-04T08:46:00Z)
• ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning [60.02503434201552]
  This research proposes learning approximations of complex exposures from training sets of simple ones. We demonstrate this approach to synthetic sensor responses surprisingly improves the detection of out-of-distribution obscured chemical analytes.
  arXiv  Detail & Related papers  (2023-02-09T20:19:57Z)
• Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport [49.237577649802034]
  We develop a novel optimal transport-based algorithm termed MROT to enhance their generalization capability for molecular regression problems. MROT significantly outperforms state-of-the-art models, showing promising potential in accelerating the discovery of new substances.
  arXiv  Detail & Related papers  (2022-02-13T04:56:18Z)
• Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra [68.8204255655161]
  We focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We uncover interesting structures in the principal component basis, namely, well-defined branches corresponding to different chemical regimes.
  arXiv  Detail & Related papers  (2022-01-07T22:26:33Z)
• MassFormer: Tandem Mass Spectrum Prediction for Small Molecules using Graph Transformers [3.2951121243459522]
  Tandem mass spectra capture fragmentation patterns that provide key structural information about a molecule. For over seventy years, spectrum prediction has remained a key challenge in the field. We propose a new model, MassFormer, for accurately predicting tandem mass spectra.
  arXiv  Detail & Related papers  (2021-11-08T20:55:15Z)
• Machine-learning-enhanced time-of-flight mass spectrometry analysis [10.16825220733013]
  We introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds. Our approach is cross-validated on mass spectra generated from different time-of-flight mass spectrometry(ToF-MS) techniques, offering the ToF-MS community an open-source, intelligent mass spectra analysis.
  arXiv  Detail & Related papers  (2020-10-02T14:35:47Z)
• Machine Learning in Nano-Scale Biomedical Engineering [77.75587007080894]
  We review the existing research regarding the use of machine learning in nano-scale biomedical engineering. The main challenges that can be formulated as ML problems are classified into the three main categories. For each of the presented methodologies, special emphasis is given to its principles, applications, and limitations.
  arXiv  Detail & Related papers  (2020-08-05T15:45:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.