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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...

Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Predicting missing proteomics values using machine learning: Filling the gap using transcriptomics and other

Juan Ochoteco Asensio¹, Marcha Verheijen¹, Florian Caiment¹

¹Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, The Netherlands.

Computational and Structural Biotechnology Journal

|May 23, 2022

Summary

This summary is machine-generated.

Machine learning can predict missing protein data using transcriptomics. This approach overcomes limitations in proteomics, offering a more comprehensive view of cellular functions.

Keywords:

Machine Learning Proteomics Rna-sequencing Transcriptomics

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Area of Science:

Molecular Biology
Bioinformatics
Computational Biology

Background:

Proteins are crucial for cellular functions, but proteomics studies have limitations in quantifying all proteins.
Transcriptomics is often used as a surrogate, but transcript and protein levels do not always correlate due to post-transcriptional regulation.
Missing protein values in proteomics data hinder comprehensive analysis.

Purpose of the Study:

To investigate the potential of machine learning regression to predict missing protein values in proteomics.
To leverage transcriptomics data, including regulatory elements like microRNAs and circular RNAs, for protein prediction.

Main Methods:

Utilized machine learning regression models, specifically random forest.
Employed transcriptomics data with various extracted features, including translational regulatory elements.
Evaluated different machine learning algorithms and data splitting strategies.

Main Results:

Random forest demonstrated good accuracy in predicting protein levels from transcriptomics data.
The model successfully predicted proteins in new, unseen samples.
Developed and shared pre-processing and model-building scripts.

Conclusions:

Machine learning, particularly random forest, can effectively predict missing protein values using transcriptomics data.
This approach offers a viable solution to overcome proteomics limitations and enhance biological insights.
The study provides accessible tools for implementing this predictive methodology.