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Proteomics

Proteomics

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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Related Experiment Video

Updated: Aug 21, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Application of Machine Learning in Spatial Proteomics.

Minjie Mou¹, Ziqi Pan¹, Mingkun Lu¹

¹College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.

Journal of Chemical Information and Modeling

|November 15, 2022

Summary

This summary is machine-generated.

Spatial proteomics uses machine learning (ML) to analyze protein localization data. This review highlights ML applications, data resources, and challenges in spatial proteomics for cell biology and medical research.

Keywords:

analytical tools cell biology data resources deep learning imaging machine learning mass spectrometry protein subcellular localization spatial proteomics

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Area of Science:

Biochemistry and Molecular Biology
Computational Biology
Bioinformatics

Background:

Spatial proteomics investigates protein localization and dynamics, crucial for cellular processes and disease.
Subcellular proteomics is a key area of focus within spatial proteomics.
Protein localization is increasingly recognized for its role in disease progression.

Purpose of the Study:

To comprehensively survey the applications of machine learning (ML) in spatial proteomics.
To introduce data resources for spatial proteome analysis.
To discuss challenges and provide guidelines for ML in spatial proteomics.

Main Methods:

Review of existing literature on ML applications in spatial proteomics.
Analysis of MS-based and imaging-based experimental approaches.
Elaboration on various ML algorithms used in spatial proteomic data analysis pipelines.

Main Results:

Introduction of diverse data resources for spatial proteome studies.
Detailed explanation of ML algorithm roles in spatial proteomic data analysis.
Presentation of successful applications and analytical tools integrating ML.

Conclusions:

ML is vital for analyzing complex spatial proteomics data.
This review offers guidance for researchers applying ML in spatial proteomics.
Findings facilitate understanding of cell biology and advance medical and drug discovery research.