Variable selection and parameter estimation of viral amplification in vero cell cultures dedicated to the production of a dengue vaccine

2018 ◽  
Vol 35 (1) ◽  
pp. e2687
Author(s):  
Thomas Abbate ◽  
Laurent Dewasme ◽  
Alain Vande Wouwer
Keyword(s):  
Parameter Estimation ◽  
Variable Selection ◽  
Vero Cell ◽  
Cell Cultures ◽  
Dengue Vaccine

scholarly journals Production of yellow fever virus in microcarrier-based Vero cell cultures

Vaccine ◽  
2009 ◽  
Vol 27 (46) ◽  
pp. 6420-6423 ◽  
Author(s):  
Marta Cristina O. Souza ◽  
Marcos S. Freire ◽  
Erica A. Schulze ◽  
Luciane P. Gaspar ◽  
Leda R. Castilho
Keyword(s):  
Vero Cell ◽  
Yellow Fever ◽  
Cell Cultures ◽  
Yellow Fever Virus ◽  
Fever Virus

scholarly journals Recovery of bluetongue virus serogroup from sera collected for a serological survey from apparently healthy cattle, from the Sudan

1986 ◽  
Vol 96 (3) ◽  
pp. 529-533 ◽  
Author(s):  
E. M. E. Abu Elzein
Keyword(s):  
Vero Cell ◽  
Cell Cultures ◽  
Bluetongue Virus ◽  
Complement Fixation ◽  
Serological Survey ◽  
Agar Gel ◽  
Sole Purpose ◽  
Healthy Cattle ◽  
Apparently Healthy

SUMMARYVirus of the bluetongue (BT) serogroup was recovered from 11% of cattle sera collected from apparently healthy animals in Khartoum Province for the sole purpose of screening for BT antibodies. Since these sera did not contain BT antibodies, the donor cattle could have been scored as BT free in the serological survey.Virus was initially isolated in chicken embryos inoculated intravascularly, and was further adapted to Vero cell cultures. Isolates were identified as belonging to the BT serogroup using the agar gel immunodiffusion (AGID) and complement fixation (CF) tests.The results indicated that cattle in the Sudan could harbour BT virus without showing symptoms of the disease. Such an observation necessitates further work to clarify the role of cattle in the epidemiology of BT in the Sudan.

scholarly journals Sparse Boosting Based Machine Learning Methods for High-Dimensional Data

2021 ◽  
Author(s):  
Mu Yue
Keyword(s):  
Machine Learning ◽  
Parameter Estimation ◽  
Variable Selection ◽  
Survival Data ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Learning Methods ◽  
Require Time ◽  
Machine Learning Methods ◽  
Boosting Method

In high-dimensional data, penalized regression is often used for variable selection and parameter estimation. However, these methods typically require time-consuming cross-validation methods to select tuning parameters and retain more false positives under high dimensionality. This chapter discusses sparse boosting based machine learning methods in the following high-dimensional problems. First, a sparse boosting method to select important biomarkers is studied for the right censored survival data with high-dimensional biomarkers. Then, a two-step sparse boosting method to carry out the variable selection and the model-based prediction is studied for the high-dimensional longitudinal observations measured repeatedly over time. Finally, a multi-step sparse boosting method to identify patient subgroups that exhibit different treatment effects is studied for the high-dimensional dense longitudinal observations. This chapter intends to solve the problem of how to improve the accuracy and calculation speed of variable selection and parameter estimation in high-dimensional data. It aims to expand the application scope of sparse boosting and develop new methods of high-dimensional survival analysis, longitudinal data analysis, and subgroup analysis, which has great application prospects.

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