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As proteins execute biological functions and their expression levels influence phenotypic outcomes, we developed a convolutional neural network (CNN) to predict protein abundances from mRNA abundances, protein sequence, and mRNA sequence in <jats:italic>Homo sapiens (H. sapiens)<\/jats:italic> and the reference plant <jats:italic>Arabidopsis thaliana (A. thaliana)<\/jats:italic>.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>After hyperparameter optimization and initial data exploration, we implemented distinct training modules for value-based and sequence-based data. By analyzing the learned weights, we revealed common and organism-specific sequence features that influence protein-to-mRNA ratios (PTRs), including known and putative sequence motifs. Adding condition-specific protein interaction information identified genes correlated with many PTRs but did not improve predictions, likely due to insufficient data. The integrated model predicted protein abundance on unseen genes with a coefficient of determination (r<jats:sup>2<\/jats:sup>) of 0.30 in <jats:italic>H. sapiens<\/jats:italic> and 0.32 in <jats:italic>A. thaliana.<\/jats:italic>\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>For <jats:italic>H. sapiens,<\/jats:italic> our model improves prediction performance by nearly 50% compared to previous sequence-based approaches, and for <jats:italic>A. thaliana<\/jats:italic> it represents the first model of its kind. The model\u2019s learned motifs recapitulate known regulatory elements, supporting its utility in systems-level and hypothesis-driven research approaches related to protein regulation.\n<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s13040-025-00434-z","type":"journal-article","created":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T12:41:41Z","timestamp":1740746501000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Inferring protein from transcript abundances using convolutional neural networks"],"prefix":"10.1186","volume":"18","author":[{"given":"Patrick Maximilian","family":"Schwehn","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pascal","family":"Falter-Braun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,27]]},"reference":[{"issue":"4","key":"434_CR1","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1111\/tpj.13520","volume":"90","author":"C Merchante","year":"2017","unstructured":"Merchante C, Stepanova AN, Alonso JM. 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