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10.1186/s12859-017-1584-1 http://scihub22266oqcxt.onion/10.1186/s12859-017-1584-1 C5351198!5351198 !28292266     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 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\28292266 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       BMC+Bioinformatics 2017 ; 18 (1 ): 170 Nephropedia Template TP {{tp|p=28292266 |t=2017. MicroRNA categorization using sequence motifs and k-mers |pdf=|usr=}}{{28292266 }} gab.com Text MicroRNA categorization using sequence motifs and k-mers JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=28292266 #FOAvip BACKGROUND: Post-transcriptional gene dysregulation can be a hallmark of diseases like cancer and microRNAs (miRNAs) play a key role in the modulation of translation efficiency. Known pre-miRNAs are listed in miRBase, and they have been discovered in a variety of organisms ranging from viruses and microbes to eukaryotic organisms. The computational detection of pre-miRNAs is of great interest, and such approaches usually employ machine learning to discriminate between miRNAs and other sequences. Many features have been proposed describing pre-miRNAs, and we have previously introduced the use of sequence motifs and k-mers as useful ones. There have been reports of xeno-miRNAs detected via next generation sequencing. However, they may be contaminations and to aid that important decision-making process, we aimed to establish a means to differentiate pre-miRNAs from different species. RESULTS: To achieve distinction into species, we used one species' pre-miRNAs as the positive and another species' pre-miRNAs as the negative training and test data for the establishment of machine learned models based on sequence motifs and k-mers as features. This approach resulted in higher accuracy values between distantly related species while species with closer relation produced lower accuracy values. CONCLUSIONS: We were able to differentiate among species with increasing success when the evolutionary distance increases. This conclusion is supported by previous reports of fast evolutionary changes in miRNAs since even in relatively closely related species a fairly good discrimination was possible. Twit Text FOAVip MicroRNA categorization using sequence motifs and k-mers ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=28292266 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28292266 #FOAvip English Wikipedia <ref>{{Cite pmid|28292266 }}</ref> MicroRNA categorization using sequence motifs and k-mers #MMPMID28292266 Yousef M ; Khalifa W ; Acar ?E ; Allmer J BMC Bioinformatics 2017[Mar]; 18 (1 ): 170 PMID28292266 show ga BACKGROUND: Post-transcriptional gene dysregulation can be a hallmark of diseases like cancer and microRNAs (miRNAs) play a key role in the modulation of translation efficiency. Known pre-miRNAs are listed in miRBase, and they have been discovered in a variety of organisms ranging from viruses and microbes to eukaryotic organisms. The computational detection of pre-miRNAs is of great interest, and such approaches usually employ machine learning to discriminate between miRNAs and other sequences. Many features have been proposed describing pre-miRNAs, and we have previously introduced the use of sequence motifs and k-mers as useful ones. There have been reports of xeno-miRNAs detected via next generation sequencing. However, they may be contaminations and to aid that important decision-making process, we aimed to establish a means to differentiate pre-miRNAs from different species. RESULTS: To achieve distinction into species, we used one species' pre-miRNAs as the positive and another species' pre-miRNAs as the negative training and test data for the establishment of machine learned models based on sequence motifs and k-mers as features. This approach resulted in higher accuracy values between distantly related species while species with closer relation produced lower accuracy values. CONCLUSIONS: We were able to differentiate among species with increasing success when the evolutionary distance increases. This conclusion is supported by previous reports of fast evolutionary changes in miRNAs since even in relatively closely related species a fairly good discrimination was possible. |Animals [MESH] |Base Sequence [MESH] |Fabaceae/classification/genetics [MESH] |High-Throughput Nucleotide Sequencing [MESH] |Humans [MESH] |MicroRNAs/chemistry/genetics/*metabolism [MESH] |Phylogeny [MESH]MicroRNA categorization using sequence motifs and k-mers (epmc).pdf DeepDyve Pubget Overpricing