Real time machine learning prediction of next generation sequencing test results in live clinical settings

Real time machine learning prediction of next generation sequencing test results in live clinical settings

Abstract Next-generation sequencing-based tests have advanced the field of medical diagnostics, but their novelty and cost can lead to uncertainty in clinical deployment. The Heme-STAMP is one such assay that tracks mutations in genes implicated in hematolymphoid neoplasms. Rather than limiting its...

Full description

Saved in:
Bibliographic Details
Main Authors: Grace Y. E. Kim, Matthew Schwede, Conor K. Corbin, Sajjad Fouladvand, Rondeep Brar, David Iberri, William Shomali, Jean S. Oak, Dita Gratzinger, Henning Stehr, Jonathan H. Chen
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:npj Digital Medicine
Online Access:https://doi.org/10.1038/s41746-025-01816-7
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1038/s41746-025-01816-7

Similar Items