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Positional SHAP (PoSHAP) for Interpretation of machine learning models trained from biological sequences

PLoS computational biology, 2022-01, Vol.18 (1), p.e1009736-e1009736

2022

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Protein Turnover in Aging and Longevity

Proteomics (Weinheim), 2018-03, Vol.18 (5-6), p.e1700108-n/a

2018

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The Mitochondrial Acylome Emerges: Proteomics, Regulation by Sirtuins, and Metabolic and Disease Implications

Cell metabolism, 2018-03, Vol.27 (3), p.497-512

2018

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Qualitative and Quantitative Shotgun Proteomics Data Analysis from Data-Dependent Acquisition Mass Spectrometry

Methods in molecular biology (Clifton, N.J.), 2021, Vol.2259, p.297

Meyer, Jesse G

2021

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Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques

Expert review of proteomics, 2017-05, Vol.14 (5), p.419-429

2017

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Deep learning neural network tools for proteomics

Cell reports methods, 2021-06, Vol.1 (2), p.100003-100003, Article 100003

Meyer, Jesse G.

2021

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Regulation of UCP1 and Mitochondrial Metabolism in Brown Adipose Tissue by Reversible Succinylation

Molecular cell, 2019-05, Vol.74 (4), p.844-857.e7

2019

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Dietary Sugars Alter Hepatic Fatty Acid Oxidation via Transcriptional and Post-translational Modifications of Mitochondrial Proteins

Cell metabolism, 2019-10, Vol.30 (4), p.735-753.e4

2019

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Temporal dynamics of liver mitochondrial protein acetylation and succinylation and metabolites due to high fat diet and/or excess glucose or fructose

PloS one, 2018-12, Vol.13 (12), p.e0208973-e0208973

2018

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Site-specific identification and quantitation of endogenous SUMO modifications under native conditions

Nature communications, 2017-10, Vol.8 (1), p.1171-11, Article 1171

2017

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An unexpectedly large count of trees in the West African Sahara and Sahel

Nature (London), 2020-11, Vol.587 (7832), p.78-82

2020

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Global detection of human variants and isoforms by deep proteome sequencing

Nature biotechnology, 2023-12, Vol.41 (12), p.1776-1786

2023

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Label-Free Quantification from Direct Infusion Shotgun Proteome Analysis (DISPA-LFQ) with CsoDIAq Software

Analytical chemistry (Washington), 2023-01, Vol.95 (2), p.677-685

2023

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Learning Drug Functions from Chemical Structures with Convolutional Neural Networks and Random Forests

Journal of chemical information and modeling, 2019-10, Vol.59 (10), p.4438-4449

2019

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Quantitative shotgun proteome analysis by direct infusion

Nature methods, 2020-12, Vol.17 (12), p.1222

2020

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CsoDIAq Software for Direct Infusion Shotgun Proteome Analysis

Analytical chemistry (Washington), 2021-09, Vol.93 (36), p.12312-12319

2021

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Peptide Correlation Analysis (PeCorA) Reveals Differential Proteoform Regulation

Journal of proteome research, 2021-04, Vol.20 (4), p.1972-1980

2021

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Multi-omic integration by machine learning (MIMaL)

Bioinformatics (Oxford, England), 2022-10, Vol.38 (21), p.4908-4918

2022

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Simultaneous Quantification of the Acetylome and Succinylome by ‘One‐Pot’ Affinity Enrichment

Proteomics (Weinheim), 2018-09, Vol.18 (17), p.e1800123-n/a

2018

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Fast Proteome Identification and Quantification from Data-Dependent Acquisition-Tandem Mass Spectrometry (DDA MS/MS) Using Free Software Tools

Methods and protocols, 2019-03, Vol.2 (1), p.8

Meyer, Jesse G

2019

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Identification of Novel Protein Lysine Acetyltransferases in Escherichia coli

mBio, 2018-10, Vol.9 (5)

2018

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The Future of Proteomics is Up in the Air: Can Ion Mobility Replace Liquid Chromatography for High Throughput Proteomics?

Journal of proteome research, 2024-05

2024

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A Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics

Journal of the American Society for Mass Spectrometry, 2023-09, Vol.34 (9), p.1858-1867

2023

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PIQED: automated identification and quantification of protein modifications from DIA-MS data

Nature methods, 2017-07, Vol.14 (7), p.646

2017

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Positional SHAP (PoSHAP) for Interpretation of machine learning models trained from biological sequences

Protein Turnover in Aging and Longevity

The Mitochondrial Acylome Emerges: Proteomics, Regulation by Sirtuins, and Metabolic and Disease Implications

Qualitative and Quantitative Shotgun Proteomics Data Analysis from Data-Dependent Acquisition Mass Spectrometry

Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques

Deep learning neural network tools for proteomics

Regulation of UCP1 and Mitochondrial Metabolism in Brown Adipose Tissue by Reversible Succinylation

Dietary Sugars Alter Hepatic Fatty Acid Oxidation via Transcriptional and Post-translational Modifications of Mitochondrial Proteins

Temporal dynamics of liver mitochondrial protein acetylation and succinylation and metabolites due to high fat diet and/or excess glucose or fructose

Site-specific identification and quantitation of endogenous SUMO modifications under native conditions

An unexpectedly large count of trees in the West African Sahara and Sahel

Global detection of human variants and isoforms by deep proteome sequencing

Label-Free Quantification from Direct Infusion Shotgun Proteome Analysis (DISPA-LFQ) with CsoDIAq Software

Learning Drug Functions from Chemical Structures with Convolutional Neural Networks and Random Forests

Quantitative shotgun proteome analysis by direct infusion

CsoDIAq Software for Direct Infusion Shotgun Proteome Analysis

Peptide Correlation Analysis (PeCorA) Reveals Differential Proteoform Regulation

Multi-omic integration by machine learning (MIMaL)

Simultaneous Quantification of the Acetylome and Succinylome by ‘One‐Pot’ Affinity Enrichment

Fast Proteome Identification and Quantification from Data-Dependent Acquisition-Tandem Mass Spectrometry (DDA MS/MS) Using Free Software Tools

Identification of Novel Protein Lysine Acetyltransferases in Escherichia coli

Large-Scale Multi-omic Analysis of COVID-19 Severity

The Future of Proteomics is Up in the Air: Can Ion Mobility Replace Liquid Chromatography for High Throughput Proteomics?

A Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics

PIQED: automated identification and quantification of protein modifications from DIA-MS data

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