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Related Experiment Video

Updated: May 25, 2025

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

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

Published on: March 1, 2024

Inferring protein from transcript abundances using convolutional neural networks.

Patrick Maximilian Schwehn¹, Pascal Falter-Braun^2,3

¹Institute of Network Biology (INET), Molecular Targets and Therapies Center (MTTC), Helmholtz Munich, Neuherberg, Germany.

|February 27, 2025

Summary

This summary is machine-generated.

This study developed a convolutional neural network (CNN) to predict protein abundance from genetic sequences, improving accuracy by nearly 50% for humans and establishing a novel method for plants.

Keywords:

Convolutional neural networks Explainable AI Protein-to-mRNA ratio Regression analysis Translational regulation

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

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Published on: October 19, 2021

Area of Science:

Computational Biology
Genomics
Proteomics

Background:

Transcript abundance is an unreliable predictor of protein abundance.
Accurate protein abundance prediction is crucial for understanding biological functions and phenotypic outcomes.

Purpose of the Study:

To develop a convolutional neural network (CNN) model for predicting protein abundances.
To predict protein abundances using mRNA abundances, protein sequences, and mRNA sequences in Homo sapiens and Arabidopsis thaliana.

Main Methods:

Implemented distinct training modules for value-based and sequence-based data.
Analyzed learned weights to identify sequence features influencing protein-to-mRNA ratios (PTRs).
Integrated condition-specific protein interaction information.

Main Results:

Identified common and organism-specific sequence motifs influencing PTRs.
The integrated model achieved a coefficient of determination (r² ) of 0.30 in H. sapiens and 0.32 in A. thaliana for predicting protein abundance on unseen genes.
Adding protein interaction data did not improve predictions due to insufficient data.

Conclusions:

The CNN model significantly improves protein abundance prediction performance compared to previous sequence-based methods.
The model's learned motifs align with known regulatory elements, supporting its use in systems-level research.
This work presents the first predictive model of its kind for A. thaliana.