Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

6.2K
Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
6.2K

You might also read

Related Articles

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

Sort by
Same author

Correction: Higher education students' perceptions of ChatGPT: A global study of early reactions.

PloS one·2026
Same author

Treatments for COVID-19 and acute respiratory infections are associated with gender and comorbidities in an Italian online survey.

PloS one·2026
Same author

Comparative Analysis of Small Nerve Fiber Density in Fibromyalgia Syndrome and Small Fiber Neuropathy.

Biomedicines·2025
Same author

Machine learning-based prediction of circuit clotting during pediatric continuous kidney replacement therapy sessions.

Pediatric nephrology (Berlin, Germany)·2025
Same author

Higher education students' perceptions of ChatGPT: A global study of early reactions.

PloS one·2025
Same author

Trigeminal reflex testing abnormalities as a predictive model for distinguishing classical and idiopathic trigeminal neuralgia.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology·2025

Related Experiment Video

Updated: Apr 28, 2026

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
07:59

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

14.5K

Epitope profiling via mixture modeling of ranked data.

Cristina Mollica1, Luca Tardella

  • 1Dipartimento di Scienze Statistiche, Sapienza Università di Roma, Piazzale A. Moro 5, (00185) Roma, Italy.

Statistics in Medicine
|June 7, 2014
PubMed
Summary

This study introduces probability models for ranked data in bioassays, offering a robust alternative to quantitative analysis when data normalization is challenging. The novel Plackett-Luce model generalization effectively handles ranked outcomes, improving experimental unit analysis.

Keywords:
EM algorithmPlackett-Luce modelepitope mappingmixture modelsmultistage ranking modelsranking data

More Related Videos

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

11.4K
Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis
09:32

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Published on: October 15, 2021

15.2K

Related Experiment Videos

Last Updated: Apr 28, 2026

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
07:59

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

14.5K
Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

11.4K
Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis
09:32

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Published on: October 15, 2021

15.2K

Area of Science:

  • Statistics
  • Bioassay Analysis
  • Computational Biology

Background:

  • Quantitative analysis of bioassay data can be hindered by challenges in selecting appropriate normalization methods for raw numerical responses.
  • Ranked data provides a valuable alternative when quantitative measurements are problematic or subject to criticism.

Purpose of the Study:

  • To propose and evaluate probability models for ranked data as a superior alternative to quantitative analysis in bioassay experiments.
  • To introduce a generalized Plackett-Luce model that incorporates the order of ranking elicitation.
  • To demonstrate the model's application using maximum likelihood estimation and model-based clustering on real bioassay data.

Main Methods:

  • Review of standard distance-based and multistage ranking models.
  • Development of a generalized Plackett-Luce model accounting for ranking order.
  • Application of maximum likelihood estimation and a hybrid expectation-maximization algorithm for parameter estimation.
  • Model-based clustering to address heterogeneity in experimental units.
  • Comparison of the proposed mixture model with existing models for random rankings.

Main Results:

  • The novel generalized Plackett-Luce model was successfully estimated using maximum likelihood on a real bioassay dataset.
  • Model-based clustering effectively addressed the heterogeneity of experimental units.
  • The proposed mixture model demonstrated competitive performance compared to alternative models for random rankings.
  • Interpretation of identified clusters provided insights into experimental unit behavior.

Conclusions:

  • Probability models for ranked data offer a robust and valuable approach for bioassay analysis, especially when quantitative normalization is difficult.
  • The generalized Plackett-Luce model provides a flexible framework for analyzing ordered ranked data.
  • Model-based clustering enhances the analysis of heterogeneous experimental units within a probabilistic framework.