Integration of high-volume molecular and imaging data for composite biomarker discovery in the study of melanoma.

ABSTRACT: In this work the effects of simple imputations are studied, regarding the integration of multimodal data originating from different patients. Two separate datasets of cutaneous melanoma are used, an image analysis (dermoscopy) dataset together with a transcriptomic one, specifically DNA microarrays. Each modality is related to a different set of patients, and four imputation methods are employed to the formation of a unified, integrative dataset. The application of backward selection together with ensemble classifiers (random forests), followed by principal components analysis and linear discriminant analysis, illustrates the implication of the imputations on feature selection and dimensionality reduction methods. The results suggest that the expansion of the feature space through the data

SUBMITTER: Moutselos K 

PROVIDER: S-EPMC3914284 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature