Next Article in Journal
Molecular Epidemiology of Mycobacterium tuberculosis Complex Strains in Urban and Slum Settings of Nairobi, Kenya
Previous Article in Journal
enChIP-Seq Analyzer: A Software Program to Analyze and Interpret enChIP-Seq Data for the Detection of Physical Interactions between Genomic Regions
Article

Detection of Target Genes for Drug Repurposing to Treat Skeletal Muscle Atrophy in Mice Flown in Spaceflight

1
Department of Electrical & Computer Engineering, University of Puerto Rico, Mayaguez, PR 00681-9000, USA
2
Biomedical Engineering, University of Puerto Rico, Mayaguez, PR 00681-9000, USA
3
Department of Mathematics, University of Puerto Rico, Rio Piedras, PR 00925-2537, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Yalda Jamshidi
Genes 2022, 13(3), 473; https://doi.org/10.3390/genes13030473
Received: 15 January 2022 / Revised: 25 February 2022 / Accepted: 3 March 2022 / Published: 8 March 2022
(This article belongs to the Topic Complex Systems and Artificial Intelligence)
Skeletal muscle atrophy is a common condition in aging, diabetes, and in long duration spaceflights due to microgravity. This article investigates multi-modal gene disease and disease drug networks via link prediction algorithms to select drugs for repurposing to treat skeletal muscle atrophy. Key target genes that cause muscle atrophy in the left and right extensor digitorum longus muscle tissue, gastrocnemius, quadriceps, and the left and right soleus muscles are detected using graph theoretic network analysis, by mining the transcriptomic datasets collected from mice flown in spaceflight made available by GeneLab. We identified the top muscle atrophy gene regulators by the Pearson correlation and Bayesian Markov blanket method. The gene disease knowledge graph was constructed using the scalable precision medicine knowledge engine. We computed node embeddings, random walk measures from the networks. Graph convolutional networks, graph neural networks, random forest, and gradient boosting methods were trained using the embeddings, network features for predicting links and ranking top gene-disease associations for skeletal muscle atrophy. Drugs were selected and a disease drug knowledge graph was constructed. Link prediction methods were applied to the disease drug networks to identify top ranked drugs for therapeutic treatment of skeletal muscle atrophy. The graph convolution network performs best in link prediction based on receiver operating characteristic curves and prediction accuracies. The key genes involved in skeletal muscle atrophy are associated with metabolic and neurodegenerative diseases. The drugs selected for repurposing using the graph convolution network method were nutrients, corticosteroids, anti-inflammatory medications, and others related to insulin. View Full-Text
Keywords: machine learning; skeletal muscle atrophy; graph convolutional neural networks; graph neural network; random forest; gradient boosting method; knowledge graphs; node embeddings; random walk; diseases; drugs; link prediction machine learning; skeletal muscle atrophy; graph convolutional neural networks; graph neural network; random forest; gradient boosting method; knowledge graphs; node embeddings; random walk; diseases; drugs; link prediction
Show Figures

Figure 1

MDPI and ACS Style

Manian, V.; Orozco-Sandoval, J.; Diaz-Martinez, V.; Janwa, H.; Agrinsoni, C. Detection of Target Genes for Drug Repurposing to Treat Skeletal Muscle Atrophy in Mice Flown in Spaceflight. Genes 2022, 13, 473. https://doi.org/10.3390/genes13030473

AMA Style

Manian V, Orozco-Sandoval J, Diaz-Martinez V, Janwa H, Agrinsoni C. Detection of Target Genes for Drug Repurposing to Treat Skeletal Muscle Atrophy in Mice Flown in Spaceflight. Genes. 2022; 13(3):473. https://doi.org/10.3390/genes13030473

Chicago/Turabian Style

Manian, Vidya, Jairo Orozco-Sandoval, Victor Diaz-Martinez, Heeralal Janwa, and Carlos Agrinsoni. 2022. "Detection of Target Genes for Drug Repurposing to Treat Skeletal Muscle Atrophy in Mice Flown in Spaceflight" Genes 13, no. 3: 473. https://doi.org/10.3390/genes13030473

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop