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10.1155/2016/6598307 http://scihub22266oqcxt.onion/10.1155/2016/6598307 C4769744!4769744 !27057545     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 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 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 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 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27057545 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Biomed+Res+Int 2016 ; 2016 (ä): 6598307 Nephropedia Template TP {{tp|p=27057545 |t=2016. Motif-Based Text Mining of Microbial Metagenome Redundancy Profiling Data for Disease Classification |pdf=|usr=}}{{27057545 }} gab.com Text Motif-Based Text Mining of Microbial Metagenome Redundancy Profiling Data for Disease Classification JO: Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=27057545 #FOAvip BACKGROUND: Text data of 16S rRNA are informative for classifications of microbiota-associated diseases. However, the raw text data need to be systematically processed so that features for classification can be defined/extracted; moreover, the high-dimension feature spaces generated by the text data also pose an additional difficulty. RESULTS: Here we present a Phylogenetic Tree-Based Motif Finding algorithm (PMF) to analyze 16S rRNA text data. By integrating phylogenetic rules and other statistical indexes for classification, we can effectively reduce the dimension of the large feature spaces generated by the text datasets. Using the retrieved motifs in combination with common classification methods, we can discriminate different samples of both pneumonia and dental caries better than other existing methods. CONCLUSIONS: We extend the phylogenetic approaches to perform supervised learning on microbiota text data to discriminate the pathological states for pneumonia and dental caries. The results have shown that PMF may enhance the efficiency and reliability in analyzing high-dimension text data. Twit Text FOAVip Motif-Based Text Mining of Microbial Metagenome Redundancy Profiling Data for Disease Classification ????Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=27057545 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27057545 #FOAvip English Wikipedia <ref>{{Cite pmid|27057545 }}</ref> Motif-Based Text Mining of Microbial Metagenome Redundancy Profiling Data for Disease Classification #MMPMID27057545 Wang Y ; Li R ; Zhou Y ; Ling Z ; Guo X ; Xie L ; Liu L Biomed Res Int 2016[]; 2016 (ä): 6598307 PMID27057545 show ga BACKGROUND: Text data of 16S rRNA are informative for classifications of microbiota-associated diseases. However, the raw text data need to be systematically processed so that features for classification can be defined/extracted; moreover, the high-dimension feature spaces generated by the text data also pose an additional difficulty. RESULTS: Here we present a Phylogenetic Tree-Based Motif Finding algorithm (PMF) to analyze 16S rRNA text data. By integrating phylogenetic rules and other statistical indexes for classification, we can effectively reduce the dimension of the large feature spaces generated by the text datasets. Using the retrieved motifs in combination with common classification methods, we can discriminate different samples of both pneumonia and dental caries better than other existing methods. CONCLUSIONS: We extend the phylogenetic approaches to perform supervised learning on microbiota text data to discriminate the pathological states for pneumonia and dental caries. The results have shown that PMF may enhance the efficiency and reliability in analyzing high-dimension text data. |*Algorithms [MESH] |Bacterial Infections/classification/*microbiology [MESH] |Cluster Analysis [MESH] |Data Mining/*methods [MESH] |Dental Caries [MESH] |Humans [MESH] |Metagenome/*genetics [MESH] |Metagenomics/*methods [MESH] |Phylogeny [MESH] |Pneumonia [MESH] |RNA, Ribosomal, 16S/genetics [MESH]Motif-Based Text Mining of Microbial Metagenome Redundancy Profiling D(epmc).pdf DeepDyve Pubget Overpricing