Advancements in human genomics have generated a surge of available data, fueling the growth and accessibility of databases for more comprehensive, in-depth genetic studies. We provide a straightforward and innovative methodology to optimize cloud configuration in order to conduct genome-wide association studies.
Sepsis is one of the leading causes of morbidity and mortality in hospitals. Early diagnosis could substantially improve the patient outcomes and reduce the mortality rate. In this paper we propose a machine learning approach for anomaly detection to aid the early detection of sepsis.
Genetic variability was detected for the general mixing ability of yield and its components, whereas variability for specific mixing ability was lower. The correlation between observed and predicted mixtures decreased with increasing mixture order. This framework constitutes a step forward to the screening for mixing ability, and could be further integrated into breeding programs for the development of intra or inter-specific crop mixtures. *Accepted 10/07/2019*