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10.1016/j.ymeth.2015.09.011 http://scihub22266oqcxt.onion/10.1016/j.ymeth.2015.09.011 C4894840!4894840 !26370280     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26370280 &cmd=llinks): Failed to open stream: HTTP request failed! 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Integrated protein function prediction by mining function associations, sequences, and protein-protein and gene-gene interaction networks |pdf=|usr=}}{{26370280 }} gab.com Text Integrated protein function prediction by mining function associations, sequences, and protein-protein and gene-gene interaction networks JO: Methods http://www.kidney.de/pget.php?&tw=1&pmid=26370280 #FOAvip MOTIVATIONS: Protein function prediction is an important and challenging problem in bioinformatics and computational biology. Functionally relevant biological information such as protein sequences, gene expression, and protein-protein interactions has been used mostly separately for protein function prediction. One of the major challenges is how to effectively integrate multiple sources of both traditional and new information such as spatial gene-gene interaction networks generated from chromosomal conformation data together to improve protein function prediction. RESULTS: In this work, we developed three different probabilistic scores (MIS, SEQ, and NET score) to combine protein sequence, function associations, and protein-protein interaction and spatial gene-gene interaction networks for protein function prediction. The MIS score is mainly generated from homologous proteins found by PSI-BLAST search, and also association rules between Gene Ontology terms, which are learned by mining the Swiss-Prot database. The SEQ score is generated from protein sequences. The NET score is generated from protein-protein interaction and spatial gene-gene interaction networks. These three scores were combined in a new Statistical Multiple Integrative Scoring System (SMISS) to predict protein function. We tested SMISS on the data set of 2011 Critical Assessment of Function Annotation (CAFA). The method performed substantially better than three base-line methods and an advanced method based on protein profile-sequence comparison, profile-profile comparison, and domain co-occurrence networks according to the maximum F-measure. Twit Text FOAVip Integrated protein function prediction by mining function associations, sequences, and protein-protein and gene-gene interaction networks ????Methods http://www.kidney.de/pget.php?&tw=1&pmid=26370280 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26370280 #FOAvip English Wikipedia <ref>{{Cite pmid|26370280 }}</ref> Integrated protein function prediction by mining function associations, sequences, and protein-protein and gene-gene interaction networks #MMPMID26370280 Cao R ; Cheng J Methods 2016[Jan]; 93 (ä): 84-91 PMID26370280 show ga MOTIVATIONS: Protein function prediction is an important and challenging problem in bioinformatics and computational biology. Functionally relevant biological information such as protein sequences, gene expression, and protein-protein interactions has been used mostly separately for protein function prediction. One of the major challenges is how to effectively integrate multiple sources of both traditional and new information such as spatial gene-gene interaction networks generated from chromosomal conformation data together to improve protein function prediction. RESULTS: In this work, we developed three different probabilistic scores (MIS, SEQ, and NET score) to combine protein sequence, function associations, and protein-protein interaction and spatial gene-gene interaction networks for protein function prediction. The MIS score is mainly generated from homologous proteins found by PSI-BLAST search, and also association rules between Gene Ontology terms, which are learned by mining the Swiss-Prot database. The SEQ score is generated from protein sequences. The NET score is generated from protein-protein interaction and spatial gene-gene interaction networks. These three scores were combined in a new Statistical Multiple Integrative Scoring System (SMISS) to predict protein function. We tested SMISS on the data set of 2011 Critical Assessment of Function Annotation (CAFA). The method performed substantially better than three base-line methods and an advanced method based on protein profile-sequence comparison, profile-profile comparison, and domain co-occurrence networks according to the maximum F-measure. |Data Mining/*methods [MESH] |Databases, Protein [MESH] |Gene Regulatory Networks/*genetics [MESH] |Protein Interaction Domains and Motifs/*genetics [MESH] |Protein Interaction Mapping/*methods [MESH]Integrated protein function prediction by mining function associations(epmc).pdf DeepDyve Pubget Overpricing