Gene expression data classification using topology and machine learning models.

ABSTRACT:

Background

Interpretation of high-throughput gene expression data continues to require mathematical tools in data analysis that recognizes the shape of the data in high dimensions. Topological data analysis (TDA) has recently been successful in extracting robust features in several applications dealing with high dimensional constructs. In this work, we utilize some recent developments in TDA to curate gene expression data. Our work differs from the predecessors in two aspects: (1) Traditional TDA pipelines use topological signatures called barcodes to enhance feature vectors which are used for classification. In contrast, this work involves curating relevant features to obtain somewhat better representatives with the help of TDA. This representatives of the entire data facilitates

SUBMITTER: Dey TK 

PROVIDER: S-EPMC9121583 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature