Add to ORKGLogistic regression for disease classification using microarray data: model selection in a large p and small n cas
Tumor is one of the deadly diseases which is frequently to be found in animals. However, identifying...
Microarrays are being increasingly used in cancer research for a better understanding of the molecul...
Quick calculation for sample size while controlling false discovery rate with application to microar...
doi:10.1093/bioinformatics/bti422 Penalized Cox regression analysis in the high-dimensional and low-...
Genomic characterization of multiple clinical phenotypes of cancer using multivariate linear regress...
Statistical assessment of functional categories of genes deregulated in pathological conditions by u...
High-dimensional data has become a major research area in the field of genetics, bioinformatics and ...
Over the last decade or so, there have been large numbers of methods published on approaches for nor...
High-dimensional data has become a major research area in the field of genetics, bioinformatics and ...
Motivation: In the context of clinical bioinformatics methods are needed for assessing the additiona...
The classification of cancer is a significant application of the DNA microarray data. Gene selection...
Background: Machine learning is a powerful approach for describing and predicting classes in microar...
Exploiting sample variability to enhance multivariate analysis of microarray dat
MOTIVATION: Microarrays are capable of determining the expression levels of thousands of genes simul...
The classification of cancer is a significant application of the DNA microarray data. Gene selection...
Tumor is one of the deadly diseases which is frequently to be found in animals. However, identifying...
Microarrays are being increasingly used in cancer research for a better understanding of the molecul...
Quick calculation for sample size while controlling false discovery rate with application to microar...
doi:10.1093/bioinformatics/bti422 Penalized Cox regression analysis in the high-dimensional and low-...
Genomic characterization of multiple clinical phenotypes of cancer using multivariate linear regress...
Statistical assessment of functional categories of genes deregulated in pathological conditions by u...
High-dimensional data has become a major research area in the field of genetics, bioinformatics and ...
Over the last decade or so, there have been large numbers of methods published on approaches for nor...
High-dimensional data has become a major research area in the field of genetics, bioinformatics and ...
Motivation: In the context of clinical bioinformatics methods are needed for assessing the additiona...
The classification of cancer is a significant application of the DNA microarray data. Gene selection...
Background: Machine learning is a powerful approach for describing and predicting classes in microar...
Exploiting sample variability to enhance multivariate analysis of microarray dat
MOTIVATION: Microarrays are capable of determining the expression levels of thousands of genes simul...
The classification of cancer is a significant application of the DNA microarray data. Gene selection...
Tumor is one of the deadly diseases which is frequently to be found in animals. However, identifying...
Microarrays are being increasingly used in cancer research for a better understanding of the molecul...
Quick calculation for sample size while controlling false discovery rate with application to microar...