Inferring protein from transcript abundances using convolutional neural networks

Background Although transcript abundance is often used as a proxy for protein abundance, it is an unreliable predictor. As proteins execute biological functions and their expression levels influence phenotypic outcomes, we developed a convolutional neural network (CNN) to predict protein abundances...

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Bibliographic Details
Published in:	BioData mining Vol. 18; no. 1; pp. 18 - 15
Main Authors:	Schwehn, Patrick Maximilian, Falter-Braun, Pascal
Format:	Journal Article
Language:	English
Published:	London BioMed Central 27.02.2025 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	Abundance Algorithms Amino acid sequence Amino acids Analysis Arabidopsis thaliana Artificial intelligence Artificial neural networks Bioinformatics Biomedical and Life Sciences Computational Biology/Bioinformatics Computer Appl. in Life Sciences Convolutional neural networks Data Mining and Knowledge Discovery Datasets Experiments Explainable AI Genes Genetic aspects Identification and classification Life Sciences Machine learning Methods Neural networks Ontology Predictions Protein-to-mRNA ratio Proteins Proteomics Regression analysis Regulatory sequences Synthesis Translational regulation Germany Explainable AI Regression analysis Protein-to-mRNA ratio Translational regulation Convolutional neural networks
ISSN:	1756-0381, 1756-0381
Online Access:	Get full text
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