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10.1186/s40246-020-00288-y http://scihub22266oqcxt.onion/10.1186/s40246-020-00288-y 33036646!7546598!33036646     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33036646&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Hum+Genomics 2020 ; 14 (1): 36 Nephropedia Template TP {{tp|p=33036646|t=2020. Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation |pdf=|usr=}}{{33036646}} gab.com Text Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation JO: Hum Genomics http://www.kidney.de/pget.php?&tw=1&pmid=33036646 #FOAvip INTRODUCTION: The course of COVID-19 varies from asymptomatic to severe in patients. The basis for this range in symptoms is unknown. One possibility is that genetic variation is partly responsible for the highly variable response. We evaluated how well a genetic risk score based on chromosomal-scale length variation and machine learning classification algorithms could predict severity of response to SARS-CoV-2 infection. METHODS: We compared 981 patients from the UK Biobank dataset who had a severe reaction to SARS-CoV-2 infection before 27 April 2020 to a similar number of age-matched patients drawn for the general UK Biobank population. For each patient, we built a profile of 88 numbers characterizing the chromosomal-scale length variability of their germ line DNA. Each number represented one quarter of the 22 autosomes. We used the machine learning algorithm XGBoost to build a classifier that could predict whether a person would have a severe reaction to COVID-19 based only on their 88-number classification. RESULTS: We found that the XGBoost classifier could differentiate between the two classes at a significant level (p = 2 . 10(-11)) as measured against a randomized control and (p = 3 . 10(-14)) as measured against the expected value of a random guessing algorithm (AUC = 0.5). However, we found that the AUC of the classifier was only 0.51, too low for a clinically useful test. CONCLUSION: Genetics play a role in the severity of COVID-19, but we cannot yet develop a useful genetic test to predict severity. Twit Text FOAVip Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation ????Hum Genomics http://www.kidney.de/pget.php?&tw=1&pmid=33036646 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33036646 #FOAvip English Wikipedia <ref>{{Cite pmid|33036646}}</ref> Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation #MMPMID33036646 Toh C; Brody JP Hum Genomics 2020[Oct]; 14 (1): 36 PMID33036646show ga INTRODUCTION: The course of COVID-19 varies from asymptomatic to severe in patients. The basis for this range in symptoms is unknown. One possibility is that genetic variation is partly responsible for the highly variable response. We evaluated how well a genetic risk score based on chromosomal-scale length variation and machine learning classification algorithms could predict severity of response to SARS-CoV-2 infection. METHODS: We compared 981 patients from the UK Biobank dataset who had a severe reaction to SARS-CoV-2 infection before 27 April 2020 to a similar number of age-matched patients drawn for the general UK Biobank population. For each patient, we built a profile of 88 numbers characterizing the chromosomal-scale length variability of their germ line DNA. Each number represented one quarter of the 22 autosomes. We used the machine learning algorithm XGBoost to build a classifier that could predict whether a person would have a severe reaction to COVID-19 based only on their 88-number classification. RESULTS: We found that the XGBoost classifier could differentiate between the two classes at a significant level (p = 2 . 10(-11)) as measured against a randomized control and (p = 3 . 10(-14)) as measured against the expected value of a random guessing algorithm (AUC = 0.5). However, we found that the AUC of the classifier was only 0.51, too low for a clinically useful test. CONCLUSION: Genetics play a role in the severity of COVID-19, but we cannot yet develop a useful genetic test to predict severity. |*Algorithms[MESH] |*Chromosome Aberrations[MESH] |*Machine Learning[MESH] |*Severity of Illness Index[MESH] |Betacoronavirus/genetics/*isolation & purification[MESH] |COVID-19[MESH] |Case-Control Studies[MESH] |Chromosomes, Human/*genetics[MESH] |Coronavirus Infections/*diagnosis/epidemiology/genetics/virology[MESH] |Datasets as Topic[MESH] |Humans[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnosis/epidemiology/genetics/virology[MESH] |Risk Factors[MESH]Evaluation of a genetic risk score for severity of COVID-19 using huma(epmc).pdf DeepDyve Pubget Overpricing