Machine learning derived genomics driven prognostication for acute myeloid leukemia with RUNX1-RUNX1T1.

doi:10.1080/10428194.2020.1798951

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Article

Machine learning derived genomics driven prognostication for acute myeloid leukemia with RUNX1-RUNX1T1.

Leukemia & lymphoma - 1 Dec 2020

Shaikh Anam Fatima, Kakirde Chinmayee, Dhamne Chetan, Bhanshe Prasanna, Joshi Swapnali, Chaudhary Shruti, Chatterjee Gaurav, Tembhare Prashant, Prasad Maya, Roy Moulik Nirmalya, Gokarn Anant, Bonda Avinash, Nayak Lingaraj, Punatkar Sachin, Jain Hasmukh, Bagal Bhausaheb, Shetty Dhanalaxmi, Sengar Manju, Narula Gaurav, Khattry Navin, Banavali Shripad, Gujral Sumeet, P G Subramanian, Patkar Nikhil

Abstract excerpt

Panel based next generation sequencing was performed on a discovery cohort of AML with RUNX1-RUNX1T1. Supervised machine learning identified NRAS mutation and absence of mutations in ASXL2, RAD21, KIT and FLT3 genes as well as a low mutation to be associated with favorable outcome. Based on this data patients were classified into favorable and poor genetic risk classes. Patients classified as poor genetic risk...

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PMID: 32757686
DOI: 10.1080/10428194.2020.1798951
PMCID: PMC7116445

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

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