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10.1016/j.cmpb.2021.105996 http://scihub22266oqcxt.onion/10.1016/j.cmpb.2021.105996 33631640!7882428!33631640     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Methods+Programs+Biomed 2021 ; 202 (ä): 105996 Nephropedia Template TP {{tp|p=33631640|t=2021. Artificial Intelligence-Based Prediction of Covid-19 Severity on the Results of Protein Profiling |pdf=|usr=}}{{33631640}} gab.com Text Artificial Intelligence-Based Prediction of Covid-19 Severity on the Results of Protein Profiling JO: Comput Methods Programs Biomed http://www.kidney.de/pget.php?&tw=1&pmid=33631640 #FOAvip BACKGROUND: COVID-19 progresses slowly and negatively affects many people. However, mild to moderate symptoms develop in most infected people, who recover without hospitalization. Therefore, the development of early diagnosis and treatment strategies is essential. One of these methods is proteomic technology based on the blood protein profiling technique. This study aims to classify three COVID-19 positive patient groups (mild, severe, and critical) and a control group based on the blood protein profiling using deep learning (DL), random forest (RF), and gradient boosted trees (GBTs). METHODS: The dataset consists of 93 samples (60 COVID-19 patients, 33 control), and 370 variables obtained from an open-source website. The current dataset contains age, gender, and 368 protein, used to predict the relationship between disease severity and proteins using DL and machine learning approaches (RF, GBTs). An evolutionary algorithm tunes hyperparameters of the models and the predictions are assessed through accuracy, sensitivity, specificity, precision, F1 score, classification error, and kappa performance metrics. RESULTS: The accuracy of RF (96.21%) was higher as compared to DL (94.73%). However, the ensemble classifier GBTs produced the highest accuracy (96.98%). TGB1BP2 in the cardiovascular II panel and MILR1 in the inflammation panel were the two most important proteins associated with disease severity. CONCLUSIONS: The proposed model (GBTs) achieved the best prediction of disease severity based on the proteins compared to the other algorithms. The results point out that changes in blood proteins associated with the severity of COVID-19 may be used in monitoring and early diagnosis/treatment of the disease. Twit Text FOAVip Artificial Intelligence-Based Prediction of Covid-19 Severity on the Results of Protein Profiling ????Comput Methods Programs Biomed http://www.kidney.de/pget.php?&tw=1&pmid=33631640 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33631640 #FOAvip English Wikipedia <ref>{{Cite pmid|33631640}}</ref> Artificial Intelligence-Based Prediction of Covid-19 Severity on the Results of Protein Profiling #MMPMID33631640 Yasar S; Colak C; Yologlu S Comput Methods Programs Biomed 2021[Apr]; 202 (ä): 105996 PMID33631640show ga BACKGROUND: COVID-19 progresses slowly and negatively affects many people. However, mild to moderate symptoms develop in most infected people, who recover without hospitalization. Therefore, the development of early diagnosis and treatment strategies is essential. One of these methods is proteomic technology based on the blood protein profiling technique. This study aims to classify three COVID-19 positive patient groups (mild, severe, and critical) and a control group based on the blood protein profiling using deep learning (DL), random forest (RF), and gradient boosted trees (GBTs). METHODS: The dataset consists of 93 samples (60 COVID-19 patients, 33 control), and 370 variables obtained from an open-source website. The current dataset contains age, gender, and 368 protein, used to predict the relationship between disease severity and proteins using DL and machine learning approaches (RF, GBTs). An evolutionary algorithm tunes hyperparameters of the models and the predictions are assessed through accuracy, sensitivity, specificity, precision, F1 score, classification error, and kappa performance metrics. RESULTS: The accuracy of RF (96.21%) was higher as compared to DL (94.73%). However, the ensemble classifier GBTs produced the highest accuracy (96.98%). TGB1BP2 in the cardiovascular II panel and MILR1 in the inflammation panel were the two most important proteins associated with disease severity. CONCLUSIONS: The proposed model (GBTs) achieved the best prediction of disease severity based on the proteins compared to the other algorithms. The results point out that changes in blood proteins associated with the severity of COVID-19 may be used in monitoring and early diagnosis/treatment of the disease. |*Artificial Intelligence[MESH] |*Protein Array Analysis[MESH] |Aged[MESH] |COVID-19/*physiopathology[MESH] |Deep Learning[MESH] |Female[MESH] |Forecasting[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH]Artificial Intelligence-Based Prediction of Covid-19 Severity on the R(epmc).pdf DeepDyve Pubget Overpricing