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
Hauptverfasser:	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
Format:	Artikel
Sprache:	eng
Schlagworte:	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
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