CaArray_golub-00236: Multiclass cancer diagnosis using tumor gene expression signatures

ABSTRACT: The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a support vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of ran

ORGANISM(S): Homo sapiens

SUBMITTER: Mervi Heiskanen 

PROVIDER: E-GEOD-68928 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress