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10.1093/bib/bbab228 http://scihub22266oqcxt.onion/10.1093/bib/bbab228 34160596!8574953!34160596     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Brief+Bioinform 2021 ; 22 (6): ä Nephropedia Template TP {{tp|p=34160596|t=2021. LSTM-PHV: prediction of human-virus protein-protein interactions by LSTM with word2vec |pdf=|usr=}}{{34160596}} gab.com Text LSTM-PHV: prediction of human-virus protein-protein interactions by LSTM with word2vec JO: Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=34160596 #FOAvip Viral infection involves a large number of protein-protein interactions (PPIs) between human and virus. The PPIs range from the initial binding of viral coat proteins to host membrane receptors to the hijacking of host transcription machinery. However, few interspecies PPIs have been identified, because experimental methods including mass spectrometry are time-consuming and expensive, and molecular dynamic simulation is limited only to the proteins whose 3D structures are solved. Sequence-based machine learning methods are expected to overcome these problems. We have first developed the LSTM model with word2vec to predict PPIs between human and virus, named LSTM-PHV, by using amino acid sequences alone. The LSTM-PHV effectively learnt the training data with a highly imbalanced ratio of positive to negative samples and achieved AUCs of 0.976 and 0.973 and accuracies of 0.984 and 0.985 on the training and independent datasets, respectively. In predicting PPIs between human and unknown or new virus, the LSTM-PHV learned greatly outperformed the existing state-of-the-art PPI predictors. Interestingly, learning of only sequence contexts as words is sufficient for PPI prediction. Use of uniform manifold approximation and projection demonstrated that the LSTM-PHV clearly distinguished the positive PPI samples from the negative ones. We presented the LSTM-PHV online web server and support data that are freely available at http://kurata35.bio.kyutech.ac.jp/LSTM-PHV. Twit Text FOAVip LSTM-PHV: prediction of human-virus protein-protein interactions by LSTM with word2vec ????Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=34160596 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34160596 #FOAvip English Wikipedia <ref>{{Cite pmid|34160596}}</ref> LSTM-PHV: prediction of human-virus protein-protein interactions by LSTM with word2vec #MMPMID34160596 Tsukiyama S; Hasan MM; Fujii S; Kurata H Brief Bioinform 2021[Nov]; 22 (6): ä PMID34160596show ga Viral infection involves a large number of protein-protein interactions (PPIs) between human and virus. The PPIs range from the initial binding of viral coat proteins to host membrane receptors to the hijacking of host transcription machinery. However, few interspecies PPIs have been identified, because experimental methods including mass spectrometry are time-consuming and expensive, and molecular dynamic simulation is limited only to the proteins whose 3D structures are solved. Sequence-based machine learning methods are expected to overcome these problems. We have first developed the LSTM model with word2vec to predict PPIs between human and virus, named LSTM-PHV, by using amino acid sequences alone. The LSTM-PHV effectively learnt the training data with a highly imbalanced ratio of positive to negative samples and achieved AUCs of 0.976 and 0.973 and accuracies of 0.984 and 0.985 on the training and independent datasets, respectively. In predicting PPIs between human and unknown or new virus, the LSTM-PHV learned greatly outperformed the existing state-of-the-art PPI predictors. Interestingly, learning of only sequence contexts as words is sufficient for PPI prediction. Use of uniform manifold approximation and projection demonstrated that the LSTM-PHV clearly distinguished the positive PPI samples from the negative ones. We presented the LSTM-PHV online web server and support data that are freely available at http://kurata35.bio.kyutech.ac.jp/LSTM-PHV. |*Host-Pathogen Interactions[MESH] |*Software[MESH] |Algorithms[MESH] |Amino Acid Sequence[MESH] |Benchmarking[MESH] |Computational Biology/*methods[MESH] |Databases, Protein[MESH] |Deep Learning[MESH] |Humans[MESH] |Protein Interaction Domains and Motifs[MESH] |Protein Interaction Mapping/*methods[MESH] |Protein Interaction Maps[MESH] |Reproducibility of Results[MESH] |Viral Proteins/*metabolism[MESH] |Virus Diseases/*metabolism/*virology[MESH]LSTM-PHV: prediction of human-virus protein-protein interactions by LS(epmc).pdf DeepDyve Pubget Overpricing