{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:23:38Z","timestamp":1783571018240,"version":"3.55.0"},"reference-count":59,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,9,16]],"date-time":"2021-09-16T00:00:00Z","timestamp":1631750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell Dev. Biol."],"abstract":"<jats:p>Muscle atrophy is a side effect of several terrestrial diseases which also affects astronauts severely in space missions due to the reduced gravity in spaceflight. An integrative graph-theoretic network-based drug repurposing methodology quantifying the interplay of key gene regulations and protein\u2013protein interactions in muscle atrophy conditions is presented. Transcriptomic datasets from mice in spaceflight from GeneLab have been extensively mined to extract the key genes that cause muscle atrophy in organ muscle tissues such as the thymus, liver, and spleen. Top muscle atrophy gene regulators are selected by Bayesian Markov blanket method and gene\u2013disease knowledge graph is constructed using the scalable precision medicine knowledge engine. A deep graph neural network is trained for predicting links in the network. The top ranked diseases are identified and drugs are selected for repurposing using drug bank resource. A disease drug knowledge graph is constructed and the graph neural network is trained for predicting new drugs. The results are compared with machine learning methods such as random forest, and gradient boosting classifiers. Network measure based methods shows that preferential attachment has good performance for link prediction in both the gene\u2013disease and disease\u2013drug graphs. The receiver operating characteristic curves, and prediction accuracies for each method show that the random walk similarity measure and deep graph neural network outperforms the other methods. Several key target genes identified by the graph neural network are associated with diseases such as cancer, diabetes, and neural disorders. The novel link prediction approach applied to the disease drug knowledge graph identifies the Monoclonal Antibodies drug therapy as suitable candidate for drug repurposing for spaceflight induced microgravity. There are a total of 21 drugs identified as possible candidates for treating muscle atrophy. Graph neural network is a promising deep learning architecture for link prediction from gene\u2013disease, and disease\u2013drug networks.<\/jats:p>","DOI":"10.3389\/fcell.2021.732370","type":"journal-article","created":{"date-parts":[[2021,9,16]],"date-time":"2021-09-16T04:59:44Z","timestamp":1631768384000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["An Integrative Network Science and Artificial Intelligence Drug Repurposing Approach for Muscle Atrophy in Spaceflight Microgravity"],"prefix":"10.3389","volume":"9","author":[{"given":"Vidya","family":"Manian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jairo","family":"Orozco-Sandoval","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victor","family":"Diaz-Martinez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,9,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-021-04082-y","article-title":"Application of network link prediction in drug discovery","volume":"22","author":"Abbas","year":"2021","journal-title":"BMC Bioinform."},{"key":"B2","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1002\/gepi.22061","article-title":"A comparison of methods for inferring causal relationships between genotype and phenotype using additional biological measurements.","volume":"41","author":"Ainsworth","year":"2017","journal-title":"Genet. 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