Sparse matrix regression (SMR) is a two-dimensional supervised feature selection method that can directly select the features on matrix data. It uses several couples of left and right regression ...

Learning the gene coexpression pattern is a central challenge for high-dimensional gene expression analysis. Recently, sparse singular value decomposition (SVD) has been used to achieve this goal.

Results A total of 395 patients and 100 patients were enrolled respectively from MIMIC-IV database and the validation cohort. The LASSO regression identified 18 significant variables. Both models ...

Figure 3 presents the results of the central Cox model and the central logistic regression model, both of which assume a fully centralized setting where all data is aggregated in a single location.

Additionally, Cox regression models were applied to analyze the association of LVMI, LVH, and adverse outcomes. Restricted cubic spline function was introduced in the Cox regression model to explore ...

Secondary endpoints include PFS by investigator, objective response rate, best overall response, duration of response, and safety. PFS and OS endpoints were analyzed using Kaplan-Meier methods. A ...

Department of Oncology, Luzhou People’s Hospital, Luzhou, China; Background: This retrospective study aimed to develop and validate a prognostic evaluation system based on the LASSO-Cox regression ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the linear support vector regression (linear SVR) technique, where the goal is to predict a single numeric ...

Weights are derived from logistic regression of the binary mediator on the exposure and confounders.29 When the assumptions of proportional hazards and non-informative censoring are met, the weighted ...