ForestSubtype: a cancer subtype identifying approach based on high-dimensional genomic data and a parallel random forest.

ABSTRACT:

Background

Cancer subtype classification is helpful for personalized cancer treatment. Although, some approaches have been developed to classifying caner subtype based on high dimensional gene expression data, it is difficult to obtain satisfactory classification results. Meanwhile, some cancers have been well studied and classified to some subtypes, which are adopt by most researchers. Hence, this priori knowledge is significant for further identifying new meaningful subtypes.

Results

In this paper, we present a combined parallel random forest and autoencoder approach for cancer subtype identification based on high dimensional gene expression data, ForestSubtype. ForestSubtype first adopts the parallel RF and the priori knowledge of cancer subtype to train a module and extr

SUBMITTER: Luo J 

PROVIDER: S-EPMC10354904 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature