Identifying cancer-associated leukocyte profiles using high-resolution flow cytometry screening and machine learning.

ABSTRACT:

Background

Machine learning (ML) is a valuable tool with the potential to aid clinical decision making. Adoption of ML to this end requires data that reliably correlates with the clinical outcome of interest; the advantage of ML is that it can model these correlations from complex multiparameter data sets that can be difficult to interpret conventionally. While currently available clinical data can be used in ML for this purpose, there exists the potential to discover new "biomarkers" that will enhance the effectiveness of ML in clinical decision making. Since the interaction of the immune system and cancer is a hallmark of tumor establishment and progression, one potential area for cancer biomarker discovery is through the investigation of cancer-related immune cell signatures. He

SUBMITTER: Simon Davis DA 

PROVIDER: S-EPMC10435879 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature