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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.
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Related Experiment Video

Updated: Jul 25, 2025

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Published on: April 11, 2019

Leveraging transformers-based language models in proteome bioinformatics.

Nguyen Quoc Khanh Le^1,2,3,4

¹Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.

|June 29, 2023

Summary

This summary is machine-generated.

Transformer-based natural language processing (NLP) models are advancing proteome bioinformatics. These advanced models enhance the analysis of complex protein data, improving accuracy and efficiency in research.

Keywords:

bioinformatics deep learning drug discovery explainable artificial intelligence natural language processing protein expression protein function prediction transformer attention

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Area of Science:

Bioinformatics
Computational Biology
Proteomics

Background:

The exponential growth of biological data necessitates advanced analytical tools.
Proteomics, the study of proteins, is a key area within bioinformatics.
Natural language processing (NLP) offers novel methods for biological data interpretation.

Purpose of the Study:

To review recent advancements in transformer-based NLP models for proteome bioinformatics.
To examine the advantages, limitations, and applications of these models.
To highlight challenges and future research directions in this emerging field.

Main Methods:

Review of current literature on transformer-based NLP models in proteomics.
Analysis of self-attention mechanisms and parallel processing capabilities.
Evaluation of model performance in various proteomic tasks.

Main Results:

Transformer models demonstrate significant potential for processing variable-length biological sequences.
Self-attention mechanisms enable capturing long-range dependencies in protein data.
These models offer improvements in accuracy and efficiency for proteomic analyses.

Conclusions:

Transformer-based NLP models are poised to revolutionize proteome bioinformatics.
Further research is needed to address current challenges and fully exploit their potential.
These models promise enhanced understanding of protein structure, function, and interactions.