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 Experiment Videos

Peptide fragment intensity statistical modeling.

Jacques Colinge1

  • 1Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 19/3, A-1090 Vienna, Austria. jcolinge@cemm.oeaw.ac.at

Analytical Chemistry
|August 24, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Circulating DNA reveals nucleosome occupancy patterns that are associated with nucleosome-DNA affinity and are affected in cancer.

Genome medicine·2026
Same author

Integrated DNA and RNA profiling refines prognostic stratification independent of therapeutic actionability in cholangiocarcinoma.

BMC cancer·2026
Same author

Statistical models to characterize colon tumor stiffness heterogeneity through representative atomic force microscopy maps.

Scientific reports·2026
Same author

Post-Transcriptional Modification Integration for Ligand-Receptor Cellular Network Inference.

Molecular & cellular proteomics : MCP·2025
Same author

Progastrin Promotes Colorectal Cancer Stem Cell-Like Properties via the Receptor PZR.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

The transcription factor RIP140 regulates interferon γ signaling in breast cancer.

International journal of cancer·2025
Same journal

The ACS at 150: The History of Analytical Chemistry Publications and a Century of Progress.

Analytical chemistry·2026
Same journal

Machine Learning-Enabled Image Analysis of Complex Chemical Mixtures: Synthetic Urine Droplets as a Test System.

Analytical chemistry·2026
Same journal

H<sub>2</sub>O<sub>2</sub>/Viscosity Tandem-Locked Fluorescent Probes Based on an In Situ Fluorophore Synthesis Strategy for Colitis Imaging and Diagnosis.

Analytical chemistry·2026
Same journal

TopoStitcher: A Geometric-Topological Structure-Guided Stitching Framework for Single-Molecule Localization Microscopy.

Analytical chemistry·2026
Same journal

Noninvasive SERS Immunosensing of Tyrosinase for Melanoma Monitoring via Microneedle Sampling Integrated with Satellite-Structured Bifunctional Nanozymes.

Analytical chemistry·2026
Same journal

Label-Free Electrochemical CRISPR Platform Gated by Allosteric Transcription Factors for Ultrasensitive Small-Molecule Detection.

Analytical chemistry·2026
See all related articles

New statistical models accurately predict peptide fragment peak intensities. These models enhance scoring for peptide database searching and de novo sequencing, offering broad applicability.

Area of Science:

  • Proteomics
  • Computational Biology
  • Statistical Modeling

Background:

  • Accurate prediction of peptide fragment peak intensities is crucial for mass spectrometry-based proteomics.
  • Existing scoring methods in peptide identification can be limited by intensity prediction accuracy.

Purpose of the Study:

  • To develop novel statistical models for peptide fragment peak intensities.
  • To improve the accuracy of scoring algorithms used in peptide database searching and de novo sequencing.

Main Methods:

  • Development of advanced statistical models for peptide fragment peak intensity prediction.
  • Implementation of these models in open-source computer programs.

Main Results:

  • Achieved unprecedented accuracy in modeling peptide fragment peak intensities.

Related Experiment Videos

  • Demonstrated the general applicability of the models to intensity-based scoring.

    • Conclusions:

    • The new statistical models offer a significant advancement in peptide identification accuracy.
    • Open-source availability facilitates broad adoption and improvement of proteomics data analysis pipelines.