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10.1186/s12920-017-0265-2 http://scihub22266oqcxt.onion/10.1186/s12920-017-0265-2 C5461528!5461528!28589854     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Med+Genomics 2017 ; 10 (Suppl 1): ä Nephropedia Template TP {{tp|p=28589854|t=2017. A disease similarity matrix based on the uniqueness of shared genes |pdf=|usr=}}{{28589854}} gab.com Text A disease similarity matrix based on the uniqueness of shared genes JO: BMC Med Genomics http://www.kidney.de/pget.php?&tw=1&pmid=28589854 #FOAvip Background: Complex diseases involve many genes, and these genes are often associated with several different illnesses. Disease similarity measurement can be based on shared genotype or phenotype. Quantifying relationships between genes can reveal previously unknown connections and form a reference base for therapy development and drug repurposing. Methods: Here we introduce a method to measure disease similarity that incorporates the uniqueness of shared genes. For each disease pair, we calculated the uniqueness score and constructed disease similarity matrices using OMIM and Disease Ontology annotation. Results: Using the Disease Ontology-based matrix, we identified several interesting connections between cancer and other disease and conditions such as malaria, along with studies to support our findings. We also found several high scoring pairwise relationships for which there was little or no literature support, highlighting potentially interesting connections warranting additional study. Conclusions: We developed a co-occurrence matrix based on gene uniqueness to examine the relationships between diseases from OMIM and DORIF data. Our similarity matrix can be used to identify potential disease relationships and to motivate further studies investigating the causal mechanisms in diseases. Twit Text FOAVip A disease similarity matrix based on the uniqueness of shared genes ????BMC Med Genomics http://www.kidney.de/pget.php?&tw=1&pmid=28589854 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28589854 #FOAvip English Wikipedia <ref>{{Cite pmid|28589854}}</ref> A disease similarity matrix based on the uniqueness of shared genes #MMPMID28589854 Carson MB; Liu C; Lu Y; Jia C; Lu H BMC Med Genomics 2017[]; 10 (Suppl 1): ä PMID28589854show ga Background: Complex diseases involve many genes, and these genes are often associated with several different illnesses. Disease similarity measurement can be based on shared genotype or phenotype. Quantifying relationships between genes can reveal previously unknown connections and form a reference base for therapy development and drug repurposing. Methods: Here we introduce a method to measure disease similarity that incorporates the uniqueness of shared genes. For each disease pair, we calculated the uniqueness score and constructed disease similarity matrices using OMIM and Disease Ontology annotation. Results: Using the Disease Ontology-based matrix, we identified several interesting connections between cancer and other disease and conditions such as malaria, along with studies to support our findings. We also found several high scoring pairwise relationships for which there was little or no literature support, highlighting potentially interesting connections warranting additional study. Conclusions: We developed a co-occurrence matrix based on gene uniqueness to examine the relationships between diseases from OMIM and DORIF data. Our similarity matrix can be used to identify potential disease relationships and to motivate further studies investigating the causal mechanisms in diseases. äA disease similarity matrix based on the uniqueness of shared genes (epmc).pdf DeepDyve Pubget Overpricing