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DeepIso: A Deep Learning Model for Peptide Feature Detection from LC-MS map
Copyright policy )DeepIso: A Deep Learning Model for Peptide Feature Detection from LC-MS map
AbstractLiquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics provides the relative different protein abundance in healthy and disease-afflicted patients, which offers the information for molecular interactions, signaling pathways, and biomarker identification to serve the drug discovery and clinical research. Typical analysis workflow begins with the peptide feature detection and intensity calculation from LC-MS map. We are the first to propose a deep learning based model, DeepIso, that combines recent advances in Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to detect peptide features of different charge states, as well as, estimate their intensity. Existing tools are designed with limited engineered features and domain-specific parameters, which are hardly updated despite a huge amount of new coming proteomic data. On the other hand, DeepIso consisting of two separate deep learning based modules, learns multiple levels of representation of high dimensional data itself through many layers of neurons, and adaptable to newly acquired data. The peptide feature list reported by our model matches with 97.43% of high quality MS/MS identifications in a benchmark dataset, which is higher than the matching produced by several widely used tools. Our results demonstrate that novel deep learning tools are desirable to advance the state-of-the-art in protein identification and quantification.
- University of Waterloo (Canada) Canada
- University of Waterloo Canada
Microsoft Academic Graph classification: Clustering high-dimensional data Computer science Quantitative proteomics Peptide Tandem mass spectrometry Convolutional neural network Liquid chromatography–mass spectrometry Feature detection (computer vision) chemistry.chemical_classification Artificial neural network business.industry Drug discovery Deep learning Pattern recognition Recurrent neural network chemistry Feature (computer vision) Artificial intelligence business
Library of Congress Subject Headings: lcsh:Medicine lcsh:Science lcsh:R lcsh:Q
Proteomics, Proteome informatics, Article, Workflow, Deep Learning, Tandem Mass Spectrometry, Machine learning, Neurons, Multidisciplinary, Proteins, Neural Networks, Computer, Peptides, Biomarkers, Chromatography, Liquid
Proteomics, Proteome informatics, Article, Workflow, Deep Learning, Tandem Mass Spectrometry, Machine learning, Neurons, Multidisciplinary, Proteins, Neural Networks, Computer, Peptides, Biomarkers, Chromatography, Liquid
Microsoft Academic Graph classification: Clustering high-dimensional data Computer science Quantitative proteomics Peptide Tandem mass spectrometry Convolutional neural network Liquid chromatography–mass spectrometry Feature detection (computer vision) chemistry.chemical_classification Artificial neural network business.industry Drug discovery Deep learning Pattern recognition Recurrent neural network chemistry Feature (computer vision) Artificial intelligence business
Library of Congress Subject Headings: lcsh:Medicine lcsh:Science lcsh:R lcsh:Q
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citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).45 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1% citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).45 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
Citations provided by BIP!Popularity provided by BIP!- Natural Sciences and Engineering Research Council of Canada (NSERC)
- University of Waterloo (Canada) Canada
- University of Waterloo Canada
AbstractLiquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics provides the relative different protein abundance in healthy and disease-afflicted patients, which offers the information for molecular interactions, signaling pathways, and biomarker identification to serve the drug discovery and clinical research. Typical analysis workflow begins with the peptide feature detection and intensity calculation from LC-MS map. We are the first to propose a deep learning based model, DeepIso, that combines recent advances in Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to detect peptide features of different charge states, as well as, estimate their intensity. Existing tools are designed with limited engineered features and domain-specific parameters, which are hardly updated despite a huge amount of new coming proteomic data. On the other hand, DeepIso consisting of two separate deep learning based modules, learns multiple levels of representation of high dimensional data itself through many layers of neurons, and adaptable to newly acquired data. The peptide feature list reported by our model matches with 97.43% of high quality MS/MS identifications in a benchmark dataset, which is higher than the matching produced by several widely used tools. Our results demonstrate that novel deep learning tools are desirable to advance the state-of-the-art in protein identification and quantification.