Matches in SemOpenAlex for { <https://semopenalex.org/work/W2891040654> ?p ?o ?g. }
- W2891040654 endingPage "449" @default.
- W2891040654 startingPage "449" @default.
- W2891040654 abstract "Tissue-specific gene expression has long been recognized as a crucial key for understanding tissue development and function. Efforts have been made in the past decade to identify tissue-specific expression profiles, such as the Human Proteome Atlas and FANTOM5. However, these studies mainly focused on “qualitatively tissue-specific expressed genes” which are highly enriched in one or a group of tissues but paid less attention to “quantitatively tissue-specific expressed genes”, which are expressed in all or most tissues but with differential expression levels. In this study, we applied machine learning algorithms to build a computational method for identifying “quantitatively tissue-specific expressed genes” capable of distinguishing 25 human tissues from their expression patterns. Our results uncovered the expression of 432 genes as optimal features for tissue classification, which were obtained with a Matthews Correlation Coefficient (MCC) of more than 0.99 yielded by a support vector machine (SVM). This constructed model was superior to the SVM model using tissue enriched genes and yielded MCC of 0.985 on an independent test dataset, indicating its good generalization ability. These 432 genes were proven to be widely expressed in multiple tissues and a literature review of the top 23 genes found that most of them support their discriminating powers. As a complement to previous studies, our discovery of these quantitatively tissue-specific genes provides insights into the detailed understanding of tissue development and function." @default.
- W2891040654 created "2018-09-27" @default.
- W2891040654 creator A5002688054 @default.
- W2891040654 creator A5008083038 @default.
- W2891040654 creator A5025077602 @default.
- W2891040654 creator A5031168962 @default.
- W2891040654 creator A5046736908 @default.
- W2891040654 creator A5076748700 @default.
- W2891040654 date "2018-09-07" @default.
- W2891040654 modified "2023-09-25" @default.
- W2891040654 title "A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes" @default.
- W2891040654 cites W1129162722 @default.
- W2891040654 cites W1426053994 @default.
- W2891040654 cites W1480282176 @default.
- W2891040654 cites W1533942137 @default.
- W2891040654 cites W1651834140 @default.
- W2891040654 cites W1756880171 @default.
- W2891040654 cites W1777721676 @default.
- W2891040654 cites W1968868999 @default.
- W2891040654 cites W1971962917 @default.
- W2891040654 cites W1974840646 @default.
- W2891040654 cites W1976582164 @default.
- W2891040654 cites W1980095085 @default.
- W2891040654 cites W1986114891 @default.
- W2891040654 cites W1988280472 @default.
- W2891040654 cites W1988973275 @default.
- W2891040654 cites W1993885417 @default.
- W2891040654 cites W1995396494 @default.
- W2891040654 cites W1997280899 @default.
- W2891040654 cites W2007986411 @default.
- W2891040654 cites W2011468431 @default.
- W2891040654 cites W2012034410 @default.
- W2891040654 cites W2036325135 @default.
- W2891040654 cites W2042632472 @default.
- W2891040654 cites W2052877842 @default.
- W2891040654 cites W2058816221 @default.
- W2891040654 cites W2059185913 @default.
- W2891040654 cites W2060035249 @default.
- W2891040654 cites W2062960717 @default.
- W2891040654 cites W2066936218 @default.
- W2891040654 cites W2069212005 @default.
- W2891040654 cites W2071292709 @default.
- W2891040654 cites W2074990743 @default.
- W2891040654 cites W2076723282 @default.
- W2891040654 cites W2077636669 @default.
- W2891040654 cites W2085048595 @default.
- W2891040654 cites W2092055925 @default.
- W2891040654 cites W2106876831 @default.
- W2891040654 cites W2108797218 @default.
- W2891040654 cites W2109108939 @default.
- W2891040654 cites W2109553965 @default.
- W2891040654 cites W2111551655 @default.
- W2891040654 cites W2117920373 @default.
- W2891040654 cites W2127549376 @default.
- W2891040654 cites W2132352688 @default.
- W2891040654 cites W2135893370 @default.
- W2891040654 cites W2153349010 @default.
- W2891040654 cites W2154053567 @default.
- W2891040654 cites W2158217645 @default.
- W2891040654 cites W2161162458 @default.
- W2891040654 cites W2164683872 @default.
- W2891040654 cites W2170299197 @default.
- W2891040654 cites W2205737115 @default.
- W2891040654 cites W2226336744 @default.
- W2891040654 cites W2234741790 @default.
- W2891040654 cites W2304195279 @default.
- W2891040654 cites W2314145700 @default.
- W2891040654 cites W2361928047 @default.
- W2891040654 cites W2394966378 @default.
- W2891040654 cites W2396875676 @default.
- W2891040654 cites W2412727135 @default.
- W2891040654 cites W2472670016 @default.
- W2891040654 cites W2513743670 @default.
- W2891040654 cites W2545647509 @default.
- W2891040654 cites W2549139405 @default.
- W2891040654 cites W2581105217 @default.
- W2891040654 cites W2588177079 @default.
- W2891040654 cites W2606616244 @default.
- W2891040654 cites W2755267339 @default.
- W2891040654 cites W2761082930 @default.
- W2891040654 cites W2766011385 @default.
- W2891040654 cites W2769591387 @default.
- W2891040654 cites W2770474929 @default.
- W2891040654 cites W2792683657 @default.
- W2891040654 cites W2799718261 @default.
- W2891040654 cites W2806655114 @default.
- W2891040654 cites W4213061224 @default.
- W2891040654 cites W4239510810 @default.
- W2891040654 cites W2593930842 @default.
- W2891040654 doi "https://doi.org/10.3390/genes9090449" @default.
- W2891040654 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6162521" @default.
- W2891040654 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30205473" @default.
- W2891040654 hasPublicationYear "2018" @default.
- W2891040654 type Work @default.
- W2891040654 sameAs 2891040654 @default.
- W2891040654 citedByCount "12" @default.
- W2891040654 countsByYear W28910406542018 @default.
- W2891040654 countsByYear W28910406542019 @default.