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2. Unsupervised machine learning improves risk stratification in newly diagnosed multiple myeloma: an analysis of the Spanish Myeloma Group

3. Validation of the Artificial Intelligence Prognostic Scoring System for Myelodysplastic Syndromes in chronic myelomonocytic leukaemia: A novel approach for improved risk stratification

5. Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms: An Analysis of the Spanish Group of Myelodysplastic Syndromes

7. PB2384: EVALUATION OF NEW MACHINE LEARNING SYSTEMS FOR PROGNOSTICATION BASED ON GENE EXPRESSION SIGNATURES IN MANTLE CELL AND PERIPHERAL T CELL LYMPHOMAS

11. P1234: A PROGNOSTIC MODEL BASED ON GENE EXPRESSION PARAMETERS PREDICTS A BETTER RESPONSE TO BORTEZOMIB-CONTAINING IMMUNOCHEMOTHERAPY IN DIFFUSE LARGE B-CELL LYMPHOMA

12. P703: THE AIPSS-MDS MACHINE LEARNING MODEL PREDICTS OVERALL SURVIVAL AND LEUKEMIC TRANSFORMATION IN CMML: AN ANALYSIS OF THE SPANISH REGISTRY OF MDS

13. Supplementary Figure from A Machine Learning Model Based on Tumor and Immune Biomarkers to Predict Undetectable MRD and Survival Outcomes in Multiple Myeloma

14. Supplementary Data from A Machine Learning Model Based on Tumor and Immune Biomarkers to Predict Undetectable MRD and Survival Outcomes in Multiple Myeloma

15. Supervised Machine Learning Improves Risk Stratification in Newly Diagnosed Myelodysplastic Syndromes: An Analysis of the Spanish Group of Myelodysplastic Syndromes

16. Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis

17. A Machine Learning Model Based on Tumor and Immune Biomarkers to Predict Undetectable MRD and Survival Outcomes in Multiple Myeloma

18. 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

19. A Novel Machine-Learning Model to Predict Early Relapse in Mantle Cell Lymphoma (MCL)

21. Beyond MIPI: Harnessing Machine Learning and Histological Subtype for Enhanced MCL Prognostication of Survival

23. The FLT3-like Gene Expression Signature Predicts Response to Quizartinib in Wild-Type FLT3 Acute Myeloid Leukemia: An Analysis of the Pethema Quiwi Trial

24. Detection of Rare Germline Variants in the Genomes of B Cell Neoplasms

25. A Machine Learning Model Based on Tumor and Immune Biomarkers to Predict Undetectable Measurable Residual Disease (MRD) in Transplant-Eligible Multiple Myeloma (MM)

26. A machine learning model based on tumor and immune biomarkers to predict undetectable MRD and survival outcomes in multiple myeloma

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