{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T01:00:47Z","timestamp":1728176447634},"reference-count":44,"publisher":"Walter de Gruyter GmbH","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,25]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The existence of high cost-consuming and high rate of drug failures suggests the promotion of drug repositioning in drug discovery. Existing drug repositioning techniques mainly focus on discovering candidate drugs for a kind of disease, and are not suitable for predicting candidate drugs for an individual sample. Type 1 diabetes mellitus (T1DM) is a disorder of glucose homeostasis caused by autoimmune destruction of the pancreatic \u03b2-cell. Here, we present a novel single sample drug repositioning approach for predicting personalized candidate drugs for T1DM. Our method is based on the observation of drug-disease associations by measuring the similarities of individualized pathway aberrance induced by disease and various drugs using a Kolmogorov-Smirnov weighted Enrichment Score algorithm. Using this method, we predicted several underlying candidate drugs for T1DM. Some of them have been reported for the treatment of diabetes mellitus, and some with a current indication to treat other diseases might be repurposed to treat T1DM. This study conducts drug discovery via detecting the functional connections among disease and drug action, on a personalized or customized basis. Our framework provides a rational way for systematic personalized drug discovery of complex diseases and contributes to the future application of custom therapeutic decisions.<\/jats:p>","DOI":"10.1515\/sagmb-2018-0052","type":"journal-article","created":{"date-parts":[[2019,7,9]],"date-time":"2019-07-09T09:05:27Z","timestamp":1562663127000},"source":"Crossref","is-referenced-by-count":2,"title":["A novel individualized drug repositioning approach for predicting personalized candidate drugs for type 1 diabetes mellitus"],"prefix":"10.1515","volume":"18","author":[{"given":"Hong","family":"Zheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","reference":[{"key":"ref281","first-page":"e175","article-title":"Evolving gene\/transcript definitions significantly alter the interpretation of GeneChip data","volume":"3","year":"2005","journal-title":"Nucleic Acids Res"},{"key":"ref371","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1126\/science.1132939","article-title":"The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease","volume":"313","year":"2006","journal-title":"Science"},{"key":"ref121","doi-asserted-by":"crossref","first-page":"D457","DOI":"10.1093\/nar\/gkv1070","article-title":"KEGG as a reference resource for gene and protein annotation","volume":"44","year":"2016","journal-title":"Nucleic Acids Res."},{"key":"ref431","doi-asserted-by":"crossref","first-page":"e28025","DOI":"10.1371\/journal.pone.0028025","article-title":"Systematic drug repositioning based on clinical side-effects","volume":"6","year":"2011","journal-title":"PLoS One"},{"key":"ref131","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1038\/nature08506","article-title":"Predicting new molecular targets for known drugs","volume":"462","year":"2009","journal-title":"Nature"},{"key":"ref141","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1142\/S0219720010004732","article-title":"Computational approaches for drug repositioning and combination therapy design","volume":"8","year":"2010","journal-title":"J. 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