A novel machine-learning-derived genetic score correlates with measurable residual disease and is highly predictive of outcome in acute myeloid leukemia with mutated NPM1
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| Veröffentlicht in: | Blood cancer journal (New York) 2019-10, Vol.9 (10), p.79-4, Article 79 |
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| creator | Patkar, Nikhil Shaikh, Anam Fatima Kakirde, Chinmayee Nathany, Shrinidhi Ramesh, Hridya Bhanshe, Prasanna Joshi, Swapnali Chaudhary, Shruti Kannan, Sadhana Khizer, Syed Hasan Chatterjee, Gaurav Tembhare, Prashant Shetty, Dhanalaxmi Gokarn, Anant Punatkar, Sachin Bonda, Avinash Nayak, Lingaraj Jain, Hasmukh Khattry, Navin Bagal, Bhausaheb Sengar, Manju Gujral, Sumeet Subramanian, Papagudi |
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| doi_str_mv | 10.1038/s41408-019-0244-2 |
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| subjects | 45/23 631/67/69 692/308/575 Adolescent Adult Aged Aged, 80 and over Algorithms Biomedical and Life Sciences Biomedicine Cancer Research Correspondence Genes, Neoplasm Hematology High-Throughput Nucleotide Sequencing - methods Humans Leukemia, Myeloid, Acute - genetics Leukemia, Myeloid, Acute - mortality Leukemia, Myeloid, Acute - pathology Machine Learning Middle Aged Mutation Neoplasm, Residual Nuclear Proteins - genetics Oncology Survival Rate Young Adult |
| title | A novel machine-learning-derived genetic score correlates with measurable residual disease and is highly predictive of outcome in acute myeloid leukemia with mutated NPM1 |
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