Detection of circulating tumor cells by means of machine learning using Smart-Seq2 sequencing.

ABSTRACT: Circulating tumor cells (CTCs) are tumor cells that separate from the solid tumor and enter the bloodstream, which can cause metastasis. Detection and enumeration of CTCs show promising potential as a predictor for prognosis in cancer patients. Furthermore, single-cells sequencing is a technique that provides genetic information from individual cells and allows to classify them precisely and reliably. Sequencing data typically comprises thousands of gene expression reads per cell, which artificial intelligence algorithms can accurately analyze. This work presents machine-learning-based classifiers that differentiate CTCs from peripheral blood mononuclear cells (PBMCs) based on single cell RNA sequencing data. We developed four tree-based models and we trained and tested them on a dataset c

SUBMITTER: Pastuszak K 

PROVIDER: S-EPMC11094170 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature