4.8 Article

Deep Learning-Assisted Peak Curation for Large-Scale LC-MS Metabolomics

Related references

Note: Only part of the references are listed.
Article Chemistry, Analytical

Deep Learning for the Precise Peak Detection in High-Resolution LC-MS Data

Arsenty D. Melnikov et al.

ANALYTICAL CHEMISTRY (2020)

Article Chemistry, Analytical

Deep Neural Networks for Classification of LC-MS Spectral Peaks

Edward D. Kantz et al.

ANALYTICAL CHEMISTRY (2019)

Article Biochemistry & Molecular Biology

WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) Data

Nico Borgsmueller et al.

METABOLITES (2019)

Article Biochemical Research Methods

3D molecular cartography using LC-MS facilitated by Optimus and 'ili software

Ivan Protsyuk et al.

NATURE PROTOCOLS (2018)

Letter Biochemical Research Methods

Bioconda: sustainable and comprehensive software distribution for the life sciences

Bjoern Gruening et al.

NATURE METHODS (2018)

Article Biochemical Research Methods

IPO: a tool for automated optimization of XCMS parameters

Gunnar Libiseller et al.

BMC BIOINFORMATICS (2015)

Article Biochemical Research Methods

MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis

Hiroshi Tsugawa et al.

NATURE METHODS (2015)

Letter Biotechnology & Applied Microbiology

A cross-platform toolkit for mass spectrometry and proteomics

Matthew C. Chambers et al.

NATURE BIOTECHNOLOGY (2012)

Article Computer Science, Interdisciplinary Applications

The NumPy Array: A Structure for Efficient Numerical Computation

Stefan van der Walt et al.

COMPUTING IN SCIENCE & ENGINEERING (2011)

Article Computer Science, Artificial Intelligence

A Survey on Transfer Learning

Sinno Jialin Pan et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2010)

Article Computer Science, Interdisciplinary Applications

IPython:: A system for interactive scientific computing

Fernando Perez et al.

COMPUTING IN SCIENCE & ENGINEERING (2007)