Add to ORKGSurvival analysis is a supervised learning technique that in the context of microarray data is most frequently used to identify genes whose expression levels are correlated with patient survival prognosis. Survival analysis is generally applied to diseased samples for the purpose of analyzing time to event, where the event can be any milestone of interest (e.g., metastases
This project concentrates on developing a nonparametric Empirical Bayes (EB) method on predicting pa...
Survival analysis is a branch of statistics and biostatistics that studies and compares the survival...
Gene expression data can be associated with various clinical outcomes. In particular, these data can...
Survival analysis is a supervised learning technique that in the context of microarray data is most ...
Microarray technology is increasingly used to identify potential biomarkers for cancer prognostics a...
This thesis developed and applied Bayesian models for the analysis of survival data. The gene expres...
Survival analysis is one of the main areas of focus in medical research in recent years. Survival an...
This dissertation is composed of three chapters that deal with fairly distinct concepts. In the firs...
DNA microarrays in conjunction with statistical models may help gain a deeper understanding of the m...
This study considered the problem of predicting survival, based on three alternative models: a singl...
In the present thesis I introduce and evaluate a new machine learning method for estimating survival...
Tumor is one of the deadly diseases which is frequently to be found in animals. However, identifying...
This chapter discusses the identification and measurement of gene expressions as prognostic indicato...
this paper are motivated and aimed at analyzing some common types of survival data from different me...
Hypothesis testing using Bayesian networks has been proven time and again to be very useful for vari...
This project concentrates on developing a nonparametric Empirical Bayes (EB) method on predicting pa...
Survival analysis is a branch of statistics and biostatistics that studies and compares the survival...
Gene expression data can be associated with various clinical outcomes. In particular, these data can...
Survival analysis is a supervised learning technique that in the context of microarray data is most ...
Microarray technology is increasingly used to identify potential biomarkers for cancer prognostics a...
This thesis developed and applied Bayesian models for the analysis of survival data. The gene expres...
Survival analysis is one of the main areas of focus in medical research in recent years. Survival an...
This dissertation is composed of three chapters that deal with fairly distinct concepts. In the firs...
DNA microarrays in conjunction with statistical models may help gain a deeper understanding of the m...
This study considered the problem of predicting survival, based on three alternative models: a singl...
In the present thesis I introduce and evaluate a new machine learning method for estimating survival...
Tumor is one of the deadly diseases which is frequently to be found in animals. However, identifying...
This chapter discusses the identification and measurement of gene expressions as prognostic indicato...
this paper are motivated and aimed at analyzing some common types of survival data from different me...
Hypothesis testing using Bayesian networks has been proven time and again to be very useful for vari...
This project concentrates on developing a nonparametric Empirical Bayes (EB) method on predicting pa...
Survival analysis is a branch of statistics and biostatistics that studies and compares the survival...
Gene expression data can be associated with various clinical outcomes. In particular, these data can...