Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer - Wikidata - awgldk - TriplyDB

Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer

Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer

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De novo peptide sequencing by deep learning Deep Omics.High-resolution mass spectra processing for the identification of different pathological tissue types of brain tumors.Mass Spectrometry Imaging: A Review of Emerging Advancements and Future Insights.Interactive Visual Exploration of 3D Mass Spectrometry Imaging Data Using Hierarchical Stochastic Neighbor Embedding Reveals Spatiomolecular Structures at Full Data Resolution.Association of Pathological Fibrosis With Renal Survival Using Deep Neural Networks.The big picture: exploring the metabolic cross-talk in cancer Towards enhanced metabolomic data analysis of mass spectrometry image: Multivariate Curve Resolution and Machine Learning

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Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer

http://www.wikidata.org/entity/Q33640189

description

2017 nî lūn-bûn

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2017 թուականի Փետրուարին հրատարակուած գիտական յօդուած

@hyw

2017 թվականի փետրվարին հրատարակված գիտական հոդված

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2017年の論文

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2017年論文

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2017年論文

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2017年論文

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2017年論文

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2017年論文

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2017年论文

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name

Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer

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Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer

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type

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Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer

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Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer

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prefLabel

Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer

@ast

Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer

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Chemical Science

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Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer

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Anna Mroz

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P2860

Cytoscape: a software environment for integrated models of biomolecular interaction networks

Phosphatidylserine is a global immunosuppressive signal in efferocytosis, infectious disease, and cancer

Multiple statistical analysis techniques corroborate intratumor heterogeneity in imaging mass spectrometry datasets of myxofibrosarcoma

Molecular analysis of tumor margins by MALDI mass spectrometry in renal carcinoma.

MALDI imaging mass spectrometry: statistical data analysis and current computational challenges

Hyperspectral visualization of mass spectrometry imaging data.

Computational cluster validation in post-genomic data analysis.

Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform.

Centering, scaling, and transformations: improving the biological information content of metabolomics data.

Reducing the dimensionality of data with neural networks

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3500-3511

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10.1039/C6SC03738K

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5

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Jeremy K. Nicholson

James M. Kinross

Kirill Veselkov

James S McKenzie

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