Various machine learning approaches have been used for HLA peptide binding prediction, including artificial neural network (ANN), decision tree, hidden Markov model (HMM), regression methods, support vector machine (SVM), and consensus methods; the latter combines with several of the former.
2) Why Ah-counting is a necessity in almost all battery-model-assisted regression methods? 3) How to establish a consistent framework of coupling in multi-physics battery models?
The cause-specific proportional hazards model and the Fine and Gray model (also called proportional subdistribution hazards model) are two regression methods often used to account for competing risks [ 8, 12].
While using Pearson correlation method, Graphical Gaussian model and regression method, we did not partition the data rather we followed the procedure as previously done in the literature.
Among the three imputation methods: predictive model (logistic regression method), propensity score method, and MCMC method, the latter is most popular method for multiple imputation of missing data and is the default method implemented in SAS.
We test two methods for inferring Genetic Association Interaction Networks GAINN) incorporating both differential co-expression effects and differential expression effects: a generalized linear model (GLM) regression method with interaction effects (reGAIN) and a Fisher test method for correlation differences (dcGAIN).
By comparing the prediction efficient of ANN model and standard regression methods, these studies all conluded that ANN model is superior to standard regression methods [ 13].
This could mean a lot of potential future clinical applications since temporal data in the ICU environment are ubiquitous but more difficult to model with statistical regression methods [ 4, 5].
Although the coefficients of BCS and EWB were respectively significant in Model 3 and Model 5 for most regression methods, they were not both significant in Model 4.
