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10.18632/oncotarget.20481 http://scihub22266oqcxt.onion/10.18632/oncotarget.20481 C5668007!5668007 !29108274     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=29108274 &cmd=llinks): Failed to open stream: HTTP request failed! 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Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes |pdf=|usr=}}{{29108274 }} gab.com Text Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes JO: Oncotarget http://www.kidney.de/pget.php?&tw=1&pmid=29108274 #FOAvip Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two sub-vectors, one is composed of 45 elements, characterizing the information entropies of the disease genes distribution over 45 chromosome substructures. This idea is supported by our observation that some substructures (e.g., the chromosome 6 p-arm) are highly preferred by disease-related protein coding genes, while some (e.g., the 21 p-arm) are not favored at all. The second sub-vector is 30-dimensional, characterizing the distribution of disease gene enriched KEGG pathways in comparison with our manually created pathway groups. The second sub-vector complements with the first one to differentiate between various diseases. Our prediction method outperforms the state-of-the-art methods on benchmark datasets for prioritizing disease related lncRNA genes. The method also works well when only the sequence information of an lncRNA gene is known, or even when a given disease has no currently recognized long non-coding genes. Twit Text FOAVip Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes ????Oncotarget http://www.kidney.de/pget.php?&tw=1&pmid=29108274 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29108274 #FOAvip English Wikipedia <ref>{{Cite pmid|29108274 }}</ref> Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes #MMPMID29108274 Peng H ; Lan C ; Liu Y ; Liu T ; Blumenstein M ; Li J Oncotarget 2017[Oct]; 8 (45 ): 78901-78916 PMID29108274 show ga Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two sub-vectors, one is composed of 45 elements, characterizing the information entropies of the disease genes distribution over 45 chromosome substructures. This idea is supported by our observation that some substructures (e.g., the chromosome 6 p-arm) are highly preferred by disease-related protein coding genes, while some (e.g., the 21 p-arm) are not favored at all. The second sub-vector is 30-dimensional, characterizing the distribution of disease gene enriched KEGG pathways in comparison with our manually created pathway groups. The second sub-vector complements with the first one to differentiate between various diseases. Our prediction method outperforms the state-of-the-art methods on benchmark datasets for prioritizing disease related lncRNA genes. The method also works well when only the sequence information of an lncRNA gene is known, or even when a given disease has no currently recognized long non-coding genes. äChromosome preference of disease genes and vectorization for the predi(epmc).pdf DeepDyve Pubget Overpricing