Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

High-Resolution Mass Spectrometry (HRMS)

High-Resolution Mass Spectrometry (HRMS)

The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Proteomics-Based Soluble Urokinase Plasminogen Activator Receptor Levels Are Associated With Adverse Cardiovascular Outcomes in the General Population: Insights From the UK Biobank.

Journal of the American Heart Association·2026

Same author

Cardioprotective therapy in type 2 diabetes guided by a proteomic risk model: A randomized trial.

American journal of preventive cardiology·2026

Same author

Racism-Related Concern for Children and Central Hemodynamics in African American Women: A Longitudinal Study.

Biopsychosocial science and medicine·2026

Same author

Age at type 2 diabetes diagnosis, mortality, and health loss in South Asians.

Diabetes research and clinical practice·2026

Same author

Dyslipidemia and therapies after heart transplantation: Current evidence and future directions.

Journal of clinical lipidology·2026

Same author

The Comparative Prognostic Value of High Sensitivity Troponin Level and Stress Testing in Patients With Stable Coronary Artery Disease.

Journal of the American College of Cardiology·2026

Same journal

Peripheral B-cell receptor repertoire predicts immune-related adverse events following immune checkpoint inhibitor therapy in advanced renal cell carcinoma.

Scientific reports·2026

Same journal

Effects of black soldier fly (Hermetia illucens L.) larvae zoocompost on the mineral element content of blue honeysuckle berries.

Scientific reports·2026

Same journal

Investigation on absorption refrigeration performance of R1243zf with imidazolium ionic liquid as the working pairs.

Scientific reports·2026

Same journal

DeepTriage-CN: integrating clinical text with vital signs for emergency department admission prediction in an aging population.

Scientific reports·2026

Same journal

Gold nanoparticles as dual-action antiviral agents: disruption of SARS-CoV-2 viral envelopes and RNA integrity.

Scientific reports·2026

Same journal

Comparison of capillary microsampling and venous blood for multi-pathogen serosurveillance.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 29, 2026

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Local false discovery rate estimation using feature reliability in LC/MS metabolomics data.

Elizabeth Y Chong¹, Yijian Huang¹, Hao Wu¹

¹Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA, 30322.

Scientific Reports

|November 25, 2015

Summary

This summary is machine-generated.

This study introduces a new method to improve feature selection in metabolomics by accounting for data reliability. This enhances the detection of true biological signals in mass spectrometry data.

More Related Videos

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Published on: March 14, 2013

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

Related Experiment Videos

Last Updated: Mar 29, 2026

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Published on: March 14, 2013

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

Area of Science:

Biostatistics
Metabolomics
Mass Spectrometry

Background:

False discovery rate (FDR) control is crucial for statistical inference in feature selection.
Mass spectrometry-based metabolomics data often contains features with varying reliability and noise.
Traditional FDR methods can reduce statistical power by treating all features equally.

Purpose of the Study:

To develop a novel method for estimating local false discovery rate (lfdr) that incorporates feature reliability in mass spectrometry-based metabolomics.
To improve the balance between sensitivity and false discovery control in feature selection.

Main Methods:

Propose a reliability index for mass spectrometry data using repeated measurements and a composite measure.
Develop a new lfdr estimation method integrating this reliability index.
Validate the method through simulations and application to a real metabolomics dataset.

Main Results:

The proposed method demonstrated a better balance between sensitivity and false discovery control compared to traditional lfdr estimation in simulations.
Application to a real dataset identified more biologically meaningful differentially expressed metabolites.

Conclusions:

Incorporating feature reliability into lfdr estimation enhances statistical power in mass spectrometry-based metabolomics.
The new method offers improved detection of true biological signals, leading to more meaningful discoveries.