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Related Concept Videos

Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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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.
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Updated: Aug 12, 2025

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ProteomicsML: An Online Platform for Community-Curated Data sets and Tutorials for Machine Learning in Proteomics.

Tobias G Rehfeldt1, Ralf Gabriels2,3, Robbin Bouwmeester2,3

  • 1Institute for Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark.

Journal of Proteome Research
|January 24, 2023
PubMed
Summary

ProteomicsML simplifies machine learning for proteomics by providing accessible data sets and tutorials for peptide behavior prediction. This resource enhances reproducibility and aids newcomers in predictive proteomics research.

Keywords:
bioinformaticscommunity platformdeep learningeducational platformmachine learningproteomics

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Area of Science:

  • Proteomics
  • Computational Biology
  • Machine Learning

Background:

  • Data acquisition and curation are major challenges in machine learning, particularly for proteomics liquid chromatography-mass spectrometry (LC-MS) data.
  • Unique and complex data processing pipelines in predictive proteomics hinder accessibility and reproducibility.
  • The emerging field of predictive proteomics requires standardized, easy-to-access resources.

Purpose of the Study:

  • To introduce ProteomicsML, an online resource for proteomics-based data sets and tutorials.
  • To simplify access to data in easy-to-process formats for machine learning applications.
  • To provide introductory and advanced materials for researchers and educators in predictive proteomics.

Main Methods:

  • Development of an online platform, ProteomicsML, to host proteomics data sets.
  • Creation of tutorials covering various physicochemical peptide properties and machine learning algorithms.
  • Establishment of a community-driven approach for data contribution and resource expansion.

Main Results:

  • ProteomicsML offers a centralized repository of curated proteomics data sets.
  • The platform provides accessible tutorials for diverse machine learning algorithms.
  • It facilitates comparison of state-of-the-art machine learning methods in proteomics.

Conclusions:

  • ProteomicsML addresses the need for accessible and reproducible data in predictive proteomics.
  • The resource supports both newcomers and experienced researchers in the field.
  • It fosters community contribution to advance machine learning applications in proteomics.