Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data.

ABSTRACT:

Background

The small number of samples and the curse of dimensionality hamper the better application of deep learning techniques for disease classification. Additionally, the performance of clustering-based feature selection algorithms is still far from being satisfactory due to their limitation in using unsupervised learning methods. To enhance interpretability and overcome this problem, we developed a novel feature selection algorithm. In the meantime, complex genomic data brought great challenges for the identification of biomarkers and therapeutic targets. The current some feature selection methods have the problem of low sensitivity and specificity in this field.

Results

In this article, we designed a multi-scale clustering-based feature selection algorithm named MCBFS

SUBMITTER: Xu D 

PROVIDER: S-EPMC7510277 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature