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10.1093/bib/bbaa144 http://scihub22266oqcxt.onion/10.1093/bib/bbaa144 32685972!7454262!32685972     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       Brief+Bioinform 2021 ; 22 (3): ä Nephropedia Template TP {{tp|p=32685972|t=2021. FoldRec-C2C: protein fold recognition by combining cluster-to-cluster model and protein similarity network |pdf=|usr=}}{{32685972}} gab.com Text FoldRec-C2C: protein fold recognition by combining cluster-to-cluster model and protein similarity network JO: Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=32685972 #FOAvip As a key for studying the protein structures, protein fold recognition is playing an important role in predicting the protein structures associated with COVID-19 and other important structures. However, the existing computational predictors only focus on the protein pairwise similarity or the similarity between two groups of proteins from 2-folds. However, the homology relationship among proteins is in a hierarchical structure. The global protein similarity network will contribute to the performance improvement. In this study, we proposed a predictor called FoldRec-C2C to globally incorporate the interactions among proteins into the prediction. For the FoldRec-C2C predictor, protein fold recognition problem is treated as an information retrieval task in nature language processing. The initial ranking results were generated by a surprised ranking algorithm Learning to Rank, and then three re-ranking algorithms were performed on the ranking lists to adjust the results globally based on the protein similarity network, including seq-to-seq model, seq-to-cluster model and cluster-to-cluster model (C2C). When tested on a widely used and rigorous benchmark dataset LINDAHL dataset, FoldRec-C2C outperforms other 34 state-of-the-art methods in this field. The source code and data of FoldRec-C2C can be downloaded from http://bliulab.net/FoldRec-C2C/download. Twit Text FOAVip FoldRec-C2C: protein fold recognition by combining cluster-to-cluster model and protein similarity network ????Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=32685972 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32685972 #FOAvip English Wikipedia <ref>{{Cite pmid|32685972}}</ref> FoldRec-C2C: protein fold recognition by combining cluster-to-cluster model and protein similarity network #MMPMID32685972 Shao J; Yan K; Liu B Brief Bioinform 2021[May]; 22 (3): ä PMID32685972show ga As a key for studying the protein structures, protein fold recognition is playing an important role in predicting the protein structures associated with COVID-19 and other important structures. However, the existing computational predictors only focus on the protein pairwise similarity or the similarity between two groups of proteins from 2-folds. However, the homology relationship among proteins is in a hierarchical structure. The global protein similarity network will contribute to the performance improvement. In this study, we proposed a predictor called FoldRec-C2C to globally incorporate the interactions among proteins into the prediction. For the FoldRec-C2C predictor, protein fold recognition problem is treated as an information retrieval task in nature language processing. The initial ranking results were generated by a surprised ranking algorithm Learning to Rank, and then three re-ranking algorithms were performed on the ranking lists to adjust the results globally based on the protein similarity network, including seq-to-seq model, seq-to-cluster model and cluster-to-cluster model (C2C). When tested on a widely used and rigorous benchmark dataset LINDAHL dataset, FoldRec-C2C outperforms other 34 state-of-the-art methods in this field. The source code and data of FoldRec-C2C can be downloaded from http://bliulab.net/FoldRec-C2C/download. |*Models, Molecular[MESH] |*Protein Folding[MESH] |Cluster Analysis[MESH] |Computational Biology/methods[MESH] |Datasets as Topic[MESH]FoldRec-C2C: protein fold recognition by combining cluster-to-cluster (epmc).pdf DeepDyve Pubget Overpricing