Accès libre

Deep Cross-Organism Generalization of the Physiological Effects of Spaceflight from Mammalian Model Organisms to Humans

Konstantinos I. Adamopoulos

Konstantinos I. Adamopoulos

  • National Technical University of Athens, School of Electrical and Computer Engineering, Biomedical Engineering LaboratoryZografou, Greece
  • Blue Marble Space Institute of Science, Space Biosciences Division, NASA Ames Research CenterMoffett Field, USA
Recherchez cet auteur sur
Sciendo|Google Scholar
Adamopoulos, Konstantinos I.
, 
Lauren M. Sanders

Lauren M. Sanders

Blue Marble Space Institute of Science, Space Biosciences Division, NASA Ames Research CenterMoffett Field, USA
Recherchez cet auteur sur
Sciendo|Google Scholar
Sanders, Lauren M.
, 
Adrienne Hoarfrost

Adrienne Hoarfrost

Department of Marine Sciences, University of GeorgiaAthens, USA
Recherchez cet auteur sur
Sciendo|Google Scholar
Hoarfrost, Adrienne
 et 
Sylvain V. Costes

Sylvain V. Costes

NASA Space Biosciences Division, NASA Ames Research CenterMoffett Field, USA
Recherchez cet auteur sur
Sciendo|Google Scholar
Costes, Sylvain V.
  
02 juil. 2025
Deep Cross-Organism Generalization of the Physiological Effects of Spaceflight from Mammalian Model Organisms to Humans's Cover Image
À propos de cet article
Article précédent
Article suivant
Citez
Télécharger la couverture
Catégorie d'article: Research Note
Publié en ligne: 02 juil. 2025
Pages: 39 - 50
DOI: https://doi.org/10.2478/gsr-2025-0003
Mots clés
© 2025 Konstantinos I. Adamopoulos et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Figure 1.

Overall study workflow.
Overall study workflow.

Figure 2.

Heatmap of significantly dysregulated genes in Mus musculus. Genes that are shaded in dark green indicate high up-regulation with a logFC value above 1.5, while genes shaded in light green indicate up-regulation with a logFC value above 1. Genes with a logFC value between −1 and 1 are non-significantly dysregulated. Conversely, genes shaded in dark red represent highly down-regulated genes with a logFC below −1.5. The analyses and results depicted in the Heatmap were conducted with consideration only for the Top 50 differentially expressed genes per study or dataset.
Heatmap of significantly dysregulated genes in Mus musculus. Genes that are shaded in dark green indicate high up-regulation with a logFC value above 1.5, while genes shaded in light green indicate up-regulation with a logFC value above 1. Genes with a logFC value between −1 and 1 are non-significantly dysregulated. Conversely, genes shaded in dark red represent highly down-regulated genes with a logFC below −1.5. The analyses and results depicted in the Heatmap were conducted with consideration only for the Top 50 differentially expressed genes per study or dataset.

Figure 3.

Enriched terms and pathways of dysregulated genes in Mus musculus. Panel a presents the enriched terms of upregulated genes, while panels b and c display the enriched terms of downregulated genes. Corresponding enriched Gene Ontology terms and KEGG pathways are depicted on the right side of each panel, with associated genes shown at the bottom. The dark green color indicates the correlations between genes and enriched terms or pathways.
Enriched terms and pathways of dysregulated genes in Mus musculus. Panel a presents the enriched terms of upregulated genes, while panels b and c display the enriched terms of downregulated genes. Corresponding enriched Gene Ontology terms and KEGG pathways are depicted on the right side of each panel, with associated genes shown at the bottom. The dark green color indicates the correlations between genes and enriched terms or pathways.

Figure 4.

Heatmap of significantly dysregulated genes in Homo sapiens. Genes that are shaded in dark green indicate high up-regulation with a logFC value above 1.5, while genes shaded in light green indicate up-regulation with a logFC value above 1. Genes with a logFC value between −1 and 1 are considered to be non-significantly dysregulated. Conversely, genes shaded in dark red represent highly down-regulated genes with a logFC below −1.5. The analyses and results depicted in the Heatmap were conducted with consideration only for the Top 50 differentially expressed genes per study or dataset.
Heatmap of significantly dysregulated genes in Homo sapiens. Genes that are shaded in dark green indicate high up-regulation with a logFC value above 1.5, while genes shaded in light green indicate up-regulation with a logFC value above 1. Genes with a logFC value between −1 and 1 are considered to be non-significantly dysregulated. Conversely, genes shaded in dark red represent highly down-regulated genes with a logFC below −1.5. The analyses and results depicted in the Heatmap were conducted with consideration only for the Top 50 differentially expressed genes per study or dataset.

Figure 5.

Enriched terms and pathways of dysregulated genes in Homo sapiens. Panel a presents the enriched terms of upregulated genes, while panel b displays the enriched terms of downregulated genes. Corresponding enriched Gene Ontology terms and KEGG pathways are depicted on the right side of each panel, with associated genes shown at the bottom. The dark green color indicates the correlations between genes and enriched terms or pathways.
Enriched terms and pathways of dysregulated genes in Homo sapiens. Panel a presents the enriched terms of upregulated genes, while panel b displays the enriched terms of downregulated genes. Corresponding enriched Gene Ontology terms and KEGG pathways are depicted on the right side of each panel, with associated genes shown at the bottom. The dark green color indicates the correlations between genes and enriched terms or pathways.

Figure 6.

Confusion Matrices of Mus musculus’ classifiers. a) Logistic Regression, b) Decision Tree, c) Neural Network, d) Gaussian Naive Bayes, e) Random Forest, f) SVM Support Vector Machine, SVC (kernel = linear). The y-axis corresponds to the true expression level, while the x-axis corresponds to the predicted expression level. Class 0 represents down-regulated genes, and class 1 represents up-regulated genes.
Confusion Matrices of Mus musculus’ classifiers. a) Logistic Regression, b) Decision Tree, c) Neural Network, d) Gaussian Naive Bayes, e) Random Forest, f) SVM Support Vector Machine, SVC (kernel = linear). The y-axis corresponds to the true expression level, while the x-axis corresponds to the predicted expression level. Class 0 represents down-regulated genes, and class 1 represents up-regulated genes.

Figure 7.

Confusion Matrices of Homo sapiens’ classifiers. a) Logistic Regression, b) Decision Tree, c) Neural Network, d) Gaussian Naive Bayes, e) Random Forest, f) SVM Support Vector Machine, SVC (kernel = linear). The y-axis corresponds to the true expression level, while the x-axis corresponds to the predicted expression level. Class 0 represents down-regulated genes, and class 1 represents up-regulated genes.
Confusion Matrices of Homo sapiens’ classifiers. a) Logistic Regression, b) Decision Tree, c) Neural Network, d) Gaussian Naive Bayes, e) Random Forest, f) SVM Support Vector Machine, SVC (kernel = linear). The y-axis corresponds to the true expression level, while the x-axis corresponds to the predicted expression level. Class 0 represents down-regulated genes, and class 1 represents up-regulated genes.

Figure 8.

Classification reports of a) Mus musculus’ and b) Homo sapiens’ classifiers, c) Classification Report of Transfer Learning application. The classification report for our Transfer Learning model reveals an enhanced accuracy of 71% compared to the NN classifier on the Homo sapiens dataset. The performances of these classifiers were evaluated and compared based on accuracy, recall, and precision metrics.
Classification reports of a) Mus musculus’ and b) Homo sapiens’ classifiers, c) Classification Report of Transfer Learning application. The classification report for our Transfer Learning model reveals an enhanced accuracy of 71% compared to the NN classifier on the Homo sapiens dataset. The performances of these classifiers were evaluated and compared based on accuracy, recall, and precision metrics.

Figure 9.

Confusion Matrices of KNN multi-classifiers and Transfer Learning application. a) KNN multi-classifier on human dataset, b) KNN multi-classifier on mouse dataset, class 0 represents down-regulated genes, class 1 represents non-significantly dysregulated genes, and class 2 represents up-regulated genes. c) Confusion matrix of Transfer Learning application, class 0 represents down-regulated genes, and class 1 represents up-regulated genes. The y-axis corresponds to the true expression level, while the x-axis corresponds to the predicted expression level.
Confusion Matrices of KNN multi-classifiers and Transfer Learning application. a) KNN multi-classifier on human dataset, b) KNN multi-classifier on mouse dataset, class 0 represents down-regulated genes, class 1 represents non-significantly dysregulated genes, and class 2 represents up-regulated genes. c) Confusion matrix of Transfer Learning application, class 0 represents down-regulated genes, and class 1 represents up-regulated genes. The y-axis corresponds to the true expression level, while the x-axis corresponds to the predicted expression level.
Langue:
Anglais
Périodicité:
2 fois par an
Sujets de la revue:
Sciences de la vie, Sciences de la vie, autres, Sciences des matériaux, Sciences des matériaux, autres, Physique, Physique, autres
RSS Feed de la revue