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10.1093/bioinformatics/btu118 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btu118 C4071197!4071197 !24590441     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 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\24590441 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Bioinformatics 2014 ; 30 (13 ): 1850-7 Nephropedia Template TP {{tp|p=24590441 |t=2014. Protein fold recognition using geometric kernel data fusion |pdf=|usr=}}{{24590441 }} gab.com Text Protein fold recognition using geometric kernel data fusion JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24590441 #FOAvip MOTIVATION: Various approaches based on features extracted from protein sequences and often machine learning methods have been used in the prediction of protein folds. Finding an efficient technique for integrating these different protein features has received increasing attention. In particular, kernel methods are an interesting class of techniques for integrating heterogeneous data. Various methods have been proposed to fuse multiple kernels. Most techniques for multiple kernel learning focus on learning a convex linear combination of base kernels. In addition to the limitation of linear combinations, working with such approaches could cause a loss of potentially useful information. RESULTS: We design several techniques to combine kernel matrices by taking more involved, geometry inspired means of these matrices instead of convex linear combinations. We consider various sequence-based protein features including information extracted directly from position-specific scoring matrices and local sequence alignment. We evaluate our methods for classification on the SCOP PDB-40D benchmark dataset for protein fold recognition. The best overall accuracy on the protein fold recognition test set obtained by our methods is ? 86.7%. This is an improvement over the results of the best existing approach. Moreover, our computational model has been developed by incorporating the functional domain composition of proteins through a hybridization model. It is observed that by using our proposed hybridization model, the protein fold recognition accuracy is further improved to 89.30%. Furthermore, we investigate the performance of our approach on the protein remote homology detection problem by fusing multiple string kernels. AVAILABILITY AND IMPLEMENTATION: The MATLAB code used for our proposed geometric kernel fusion frameworks are publicly available at http://people.cs.kuleuven.be/?raf.vandebril/homepage/software/geomean.php?menu=5/. Twit Text FOAVip Protein fold recognition using geometric kernel data fusion ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24590441 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24590441 #FOAvip English Wikipedia <ref>{{Cite pmid|24590441 }}</ref> Protein fold recognition using geometric kernel data fusion #MMPMID24590441 Zakeri P ; Jeuris B ; Vandebril R ; Moreau Y Bioinformatics 2014[Jul]; 30 (13 ): 1850-7 PMID24590441 show ga MOTIVATION: Various approaches based on features extracted from protein sequences and often machine learning methods have been used in the prediction of protein folds. Finding an efficient technique for integrating these different protein features has received increasing attention. In particular, kernel methods are an interesting class of techniques for integrating heterogeneous data. Various methods have been proposed to fuse multiple kernels. Most techniques for multiple kernel learning focus on learning a convex linear combination of base kernels. In addition to the limitation of linear combinations, working with such approaches could cause a loss of potentially useful information. RESULTS: We design several techniques to combine kernel matrices by taking more involved, geometry inspired means of these matrices instead of convex linear combinations. We consider various sequence-based protein features including information extracted directly from position-specific scoring matrices and local sequence alignment. We evaluate our methods for classification on the SCOP PDB-40D benchmark dataset for protein fold recognition. The best overall accuracy on the protein fold recognition test set obtained by our methods is ? 86.7%. This is an improvement over the results of the best existing approach. Moreover, our computational model has been developed by incorporating the functional domain composition of proteins through a hybridization model. It is observed that by using our proposed hybridization model, the protein fold recognition accuracy is further improved to 89.30%. Furthermore, we investigate the performance of our approach on the protein remote homology detection problem by fusing multiple string kernels. AVAILABILITY AND IMPLEMENTATION: The MATLAB code used for our proposed geometric kernel fusion frameworks are publicly available at http://people.cs.kuleuven.be/?raf.vandebril/homepage/software/geomean.php?menu=5/. |*Protein Folding [MESH] |Algorithms [MESH] |Pattern Recognition, Automated/methods [MESH] |Position-Specific Scoring Matrices [MESH] |Proteins/*chemistry/metabolism [MESH]Protein fold recognition using geometric kernel data fusion (epmc).pdf DeepDyve Pubget Overpricing