Developing Robust Predictive Models for Head and Neck Cancer across Microarray and RNA-seq Data.

ABSTRACT: Increased understanding of the transcriptomic patterns underlying head and neck squamous cell carcinoma (HNSCC) can facilitate earlier diagnosis and better treatment outcomes. Integrating knowledge from multiple studies is necessary to identify fundamental, consistent gene expression signatures that distinguish HNSCC patient samples from disease-free samples, and particularly for detecting HNSCC at an early pathological stage. This study utilizes feature integration and heterogeneous ensemble modeling techniques to develop robust models for predicting HNSCC disease status in both microarray and RNAseq datasets. Several alternative models demonstrated good performance, with MCC and AUC values exceeding 0.8. These models were also applied to discriminate between early pathological stage HNSC

SUBMITTER: Kaddi CD 

PROVIDER: S-EPMC5859557 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature