20 results on '"Mosquera Orgueira, Adrian"'
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2. The implication of next-generation sequencing in the diagnosis and clinical management of non-Hodgkin lymphomas
3. Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms: An Analysis of the Spanish Group of Myelodysplastic Syndromes
4. AIPSS‐MF machine learning prognostic score validation in a cohort of myelofibrosis patients treated with ruxolitinib
5. PB2384: EVALUATION OF NEW MACHINE LEARNING SYSTEMS FOR PROGNOSTICATION BASED ON GENE EXPRESSION SIGNATURES IN MANTLE CELL AND PERIPHERAL T CELL LYMPHOMAS
6. PB2624: CLINICAL WHOLE EXOM SEQUENCING FOR CONGENITAL PLATELET DISORDERS: ONE SINGLE CENTER EXPERIENCE
7. P1047: AIPSS-MF MACHINE LEARNING MODEL AS USEFUL PROGNOSTIC SCORE COMPARED TO IPSS IN THE SETTING OF MYELOFIBROSIS PATIENTS TREATED WITH RUXOLITINIB
8. P967: A MACHINE LEARNING MODEL FOR RISK PREDICTION IN MULTIPLE MYELOMA PROGRESSING AFTER THE FIRST LINE OF THERAPY
9. P1234: A PROGNOSTIC MODEL BASED ON GENE EXPRESSION PARAMETERS PREDICTS A BETTER RESPONSE TO BORTEZOMIB-CONTAINING IMMUNOCHEMOTHERAPY IN DIFFUSE LARGE B-CELL LYMPHOMA
10. P703: THE AIPSS-MDS MACHINE LEARNING MODEL PREDICTS OVERALL SURVIVAL AND LEUKEMIC TRANSFORMATION IN CMML: AN ANALYSIS OF THE SPANISH REGISTRY OF MDS
11. Supervised Machine Learning Improves Risk Stratification in Newly Diagnosed Myelodysplastic Syndromes: An Analysis of the Spanish Group of Myelodysplastic Syndromes
12. Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis
13. A Machine Learning Model Based on Tumor and Immune Biomarkers to Predict Undetectable MRD and Survival Outcomes in Multiple Myeloma
14. Exploring Potential Molecular Mechanisms of Drug Response in FLT3-ITD Negative AML Patients Treated with Quizartinib Vs Placebo Plus Standard Chemotherapy in the Quiwi Trial
15. A Novel Machine-Learning Model to Predict Early Relapse in Mantle Cell Lymphoma (MCL)
16. Validation and Retraining of the Stellae-123 Gene Expression Signature Improved Risk Stratification in Taiwanese Acute Myeloid Leukemia Patients
17. Beyond MIPI: Harnessing Machine Learning and Histological Subtype for Enhanced MCL Prognostication of Survival
18. Epigenetic Profiling and Machine Learning for Enhanced Risk Stratification in Pediatric Acute Lymphoblastic Leukemia
19. The FLT3-like Gene Expression Signature Predicts Response to Quizartinib in Wild-Type FLT3 Acute Myeloid Leukemia: An Analysis of the Pethema Quiwi Trial
20. A Machine Learning Model Based on Tumor and Immune Biomarkers to Predict Undetectable Measurable Residual Disease (MRD) in Transplant-Eligible Multiple Myeloma (MM)
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