Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1186/s40246-020-00297-x http://scihub22266oqcxt.onion/10.1186/s40246-020-00297-x 33386081!7775839!33386081     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Hum+Genomics 2021 ; 15 (1): 1 Nephropedia Template TP {{tp|p=33386081|t=2021. Network machine learning maps phytochemically rich "Hyperfoods" to fight COVID-19 |pdf=|usr=}}{{33386081}} gab.com Text Network machine learning maps phytochemically rich "Hyperfoods" to fight COVID-19 JO: Hum Genomics http://www.kidney.de/pget.php?&tw=1&pmid=33386081 #FOAvip In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80-85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially "repurposed" against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a "food map" with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases. Twit Text FOAVip Network machine learning maps phytochemically rich "Hyperfoods" to fight COVID-19 ????Hum Genomics http://www.kidney.de/pget.php?&tw=1&pmid=33386081 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33386081 #FOAvip English Wikipedia <ref>{{Cite pmid|33386081}}</ref> Network machine learning maps phytochemically rich "Hyperfoods" to fight COVID-19 #MMPMID33386081 Laponogov I; Gonzalez G; Shepherd M; Qureshi A; Veselkov D; Charkoftaki G; Vasiliou V; Youssef J; Mirnezami R; Bronstein M; Veselkov K Hum Genomics 2021[Jan]; 15 (1): 1 PMID33386081show ga In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80-85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially "repurposed" against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a "food map" with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases. |*Functional Food[MESH] |*Machine Learning[MESH] |COVID-19/*diet therapy/virology[MESH] |Databases, Factual[MESH] |Genes, Viral[MESH] |Humans[MESH]Network machine learning maps phytochemically rich "Hyperfoods" to fig(epmc).pdf DeepDyve Pubget Overpricing