Using Machine Learning and miRNA for the Diagnosis of Esophageal Cancer.

Journal: The journal of applied laboratory medicine

Published Date: Jul 1, 2024

Abstract

BACKGROUND: Esophageal cancer (EC) remains a global health challenge, often diagnosed at advanced stages, leading to high mortality rates. Current diagnostic tools for EC are limited in their efficacy. This study aims to harness the potential of microRNAs (miRNAs) as novel, noninvasive diagnostic biomarkers for EC. Our objective was to determine the diagnostic accuracy of miRNAs, particularly in distinguishing miRNAs associated with EC from control miRNAs.

Authors

Vishnu A Aravind

REHS program, San Diego Supercomputer Center, UC San Diego, San Diego, CA, United States.
Valentina L Kouznetsova

San Diego Supercomputer Center, University of California at San Diego, La Jolla, CA 92093, USA.
Santosh Kesari

Pacific Neuroscience Institute, Santa Monica, CA 90404, USA.
Igor F Tsigelny

San Diego Supercomputer Center, University of California at San Diego, La Jolla, CA 92093, USA.

Keywords

Bayes Theorem Biomarkers, Tumor Esophageal Neoplasms Humans Machine Learning MicroRNAs Neural Networks, Computer

External Resources

View on PubMed Access via DOI PubMed (38721901)

Using Machine Learning and miRNA for the Diagnosis of Esophageal Cancer.

Abstract

Authors

Keywords

External Resources

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