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10.1093/bioinformatics/btx271 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btx271 C5870652!5870652!28882003     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       Bioinformatics 2017 ; 33 (14): i261-6 Nephropedia Template TP {{tp|p=28882003|t=2017. miniMDS: 3D structural inference from high-resolution Hi-C data |pdf=|usr=}}{{28882003}} gab.com Text miniMDS: 3D structural inference from high-resolution Hi-C data JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=28882003 #FOAvip Motivation: Recent experiments have provided Hi-C data at resolution as high as 1 kbp. However, 3D structural inference from high-resolution Hi-C datasets is often computationally unfeasible using existing methods. Results: We have developed miniMDS, an approximation of multidimensional scaling (MDS) that partitions a Hi-C dataset, performs high-resolution MDS separately on each partition, and then reassembles the partitions using low-resolution MDS. miniMDS is faster, more accurate, and uses less memory than existing methods for inferring the human genome at high resolution (10 kbp). Availability and implementation: A Python implementation of miniMDS is available on GitHub: https://github.com/seqcode/miniMDS. Supplementary information: Supplementary data are available at Bioinformatics online. Twit Text FOAVip miniMDS: 3D structural inference from high-resolution Hi-C data ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=28882003 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28882003 #FOAvip English Wikipedia <ref>{{Cite pmid|28882003}}</ref> miniMDS: 3D structural inference from high-resolution Hi-C data #MMPMID28882003 Rieber L; Mahony S Bioinformatics 2017[Jul]; 33 (14): i261-6 PMID28882003show ga Motivation: Recent experiments have provided Hi-C data at resolution as high as 1 kbp. However, 3D structural inference from high-resolution Hi-C datasets is often computationally unfeasible using existing methods. Results: We have developed miniMDS, an approximation of multidimensional scaling (MDS) that partitions a Hi-C dataset, performs high-resolution MDS separately on each partition, and then reassembles the partitions using low-resolution MDS. miniMDS is faster, more accurate, and uses less memory than existing methods for inferring the human genome at high resolution (10 kbp). Availability and implementation: A Python implementation of miniMDS is available on GitHub: https://github.com/seqcode/miniMDS. Supplementary information: Supplementary data are available at Bioinformatics online. äminiMDS: 3D structural inference from high-resolution Hi-C data (epmc).pdf DeepDyve Pubget Overpricing