Search logs in a timely and efficient manner are an important part of SRE (Site Reliability Engineer). Logs help us solve the problems during our development work. In this paper, we will introduce you a way how to bui...Search logs in a timely and efficient manner are an important part of SRE (Site Reliability Engineer). Logs help us solve the problems during our development work. In this paper, we will introduce you a way how to build an efficient logs analysis system based on kafka and Elastic Search. We hope you can learn something through the iteration of the Version and get some inspiration with your own log analysis system.展开更多
Phosphorylation of protein is an important post-translational modification that enables activation of various enzymes and receptors included in signaling pathways. To reduce the cost of identifying phosphorylation sit...Phosphorylation of protein is an important post-translational modification that enables activation of various enzymes and receptors included in signaling pathways. To reduce the cost of identifying phosphorylation site by laborious experiments, computational prediction of it has been actively studied. In this study, by adopting a new set of features and applying feature selection by Random Forest with grid search before training by Support Vector Machine, our method achieved better or comparable performance of phosphorylation site prediction for two different data sets.展开更多
文摘Search logs in a timely and efficient manner are an important part of SRE (Site Reliability Engineer). Logs help us solve the problems during our development work. In this paper, we will introduce you a way how to build an efficient logs analysis system based on kafka and Elastic Search. We hope you can learn something through the iteration of the Version and get some inspiration with your own log analysis system.
文摘Phosphorylation of protein is an important post-translational modification that enables activation of various enzymes and receptors included in signaling pathways. To reduce the cost of identifying phosphorylation site by laborious experiments, computational prediction of it has been actively studied. In this study, by adopting a new set of features and applying feature selection by Random Forest with grid search before training by Support Vector Machine, our method achieved better or comparable performance of phosphorylation site prediction for two different data sets.