1. Machine learning derived genomics driven prognostication for acute myeloid leukemia with RUNX1-RUNX1T1
- Author
-
Nirmalya Roy Moulik, Anam Fatima Shaikh, Gaurav Chatterjee, Chinmayee Kakirde, Shruti Chaudhary, Nikhil Patkar, Prashant Tembhare, Subramanian P G, Chetan Dhamne, Maya Prasad, Avinash Bonda, Sumeet Gujral, Bhausaheb Bagal, Hasmukh Jain, Lingaraj Nayak, Shripad Banavali, Navin Khattry, Anant Gokarn, Prasanna Bhanshe, Swapnali Joshi, Dhanalaxmi Shetty, Sachin Punatkar, Gaurav Narula, and Manju Sengar
- Subjects
Neuroblastoma RAS viral oncogene homolog ,Cancer Research ,business.industry ,Myeloid leukemia ,Genomics ,Hematology ,Biology ,Machine learning ,computer.software_genre ,DNA sequencing ,03 medical and health sciences ,0302 clinical medicine ,Oncology ,hemic and lymphatic diseases ,030220 oncology & carcinogenesis ,Runx1 runx1t1 ,Cohort ,Mutation (genetic algorithm) ,Artificial intelligence ,business ,computer ,030215 immunology - Abstract
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, ...
- Published
- 2020
- Full Text
- View/download PDF