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Given the absence of approved pharmacological therapies for sarcopenia, the need to discover effective pharmacological interventions has become critical.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      To address this challenge and discover new therapies, we developed a novel\n                      <jats:bold>M<\/jats:bold>\n                      ulti\u2010\n                      <jats:bold>T<\/jats:bold>\n                      ask\n                      <jats:bold>A<\/jats:bold>\n                      ttention\u2010aware method for\n                      <jats:bold>M<\/jats:bold>\n                      ulti\u2212\n                      <jats:bold>O<\/jats:bold>\n                      mics data (MTA\u2212MO) to extract complex biological insights from various biomedical data sources, including transcriptome, methylome and genome data to identify drug targets and discover new therapies. Additionally, MTA\u2010MO integrates human protein\u2013protein interaction (PPI) networks and drug\u2010target networks to improve target identification. The novel method is applied to a multi\u2010omics dataset that included 1055 participants aged 20\u201350 (mean (\u00b1\u2009SD) age 36.88 (\u00b1\u20098.64)), comprising 37.82% African\u2010American and 62.18% Caucasian\/White individuals. Physical activity levels were self\u2010reported and categorized into three groups: \u2265\u20093 times\/week, &lt;\u20093 times\/week and no regular exercise. Mean (\u00b1\u2009SD) measures for grip strength, appendicular lean mass (ALM), exercise frequency and smoking status (no\/yes,\n                      <jats:italic>n<\/jats:italic>\n                      (%)) were 38.72 (\u00b1\u20098.93) kg, 28.65 (\u00b1\u20094.63) kg, 4.31 (\u00b1\u20091.79) and 30.81%\/69.19%, respectively. Significant differences (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05) were found between groups in age, ALM, smoking, and consumption of milk, alcohol, beer and wine.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Using the MTA\u2010MO method, we identified 639 gene targets, and by analysing PPIs and querying public databases, we narrowed this list down to seven potential hub genes associated with sarcopenia (\n                      <jats:italic>ESR1<\/jats:italic>\n                      ,\n                      <jats:italic>ATM<\/jats:italic>\n                      ,\n                      <jats:italic>CDC42<\/jats:italic>\n                      ,\n                      <jats:italic>EP300<\/jats:italic>\n                      ,\n                      <jats:italic>PIK3CA<\/jats:italic>\n                      ,\n                      <jats:italic>EGF<\/jats:italic>\n                      and\n                      <jats:italic>PTK2B)<\/jats:italic>\n                      . These findings were further validated through diverse levels of pathobiological evidence associated with sarcopenia. Gene Ontology and KEGG pathways analysis highlighted five key functions and signalling pathways relevant to skeletal muscle. The interaction network analysis identified three transcriptional factors (\n                      <jats:italic>GATA2<\/jats:italic>\n                      ,\n                      <jats:italic>JUN<\/jats:italic>\n                      and\n                      <jats:italic>FOXC1<\/jats:italic>\n                      ) as the key transcriptional regulators of the seven potential genes. In silico analysis of 1940 drug candidates identified canagliflozin as a promising candidate for repurposing in sarcopenia, demonstrating the strongest binding affinity to the PTK2B protein (inhibition constant 6.97\u2009\u03bcM). This binding is stabilized by hydrophobic bonds, Van der Waals forces, pi\u2010alkyl interactions and pi\u2010anion interactions around PTK2B's active residues, suggesting its potential as a therapeutic option.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our novel approach effectively integrates multi\u2010omics data to identify potential treatments for sarcopenia. The findings suggest that canagliflozin could be a promising therapeutic candidate for sarcopenia.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jcsm.13661","type":"journal-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T02:07:18Z","timestamp":1741745238000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["An Attention\u2010Aware Multi\u2010Task Learning Framework Identifies Candidate Targets for Drug Repurposing in Sarcopenia"],"prefix":"10.1002","volume":"16","author":[{"given":"Md\u00a0Selim","family":"Reza","sequence":"first","affiliation":[{"name":"Deming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and Genomics Tulane University  New Orleans Louisiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuan","family":"Qiu","sequence":"additional","affiliation":[{"name":"Deming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and Genomics Tulane University  New Orleans Louisiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Lin","sequence":"additional","affiliation":[{"name":"Shunde Hospital of Southern Medical University  Foshan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuan\u2010Jui","family":"Su","sequence":"additional","affiliation":[{"name":"Deming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and Genomics Tulane University  New Orleans Louisiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anqi","family":"Liu","sequence":"additional","affiliation":[{"name":"Deming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and Genomics Tulane University  New Orleans Louisiana 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