Publicacións nas que colabora con Manuel Mateo Pérez Encinas (19)

2024

  1. Impact of somatic gene mutations on the risk of thrombosis in myelofibrosis

    Leukemia

  2. Integrating AIPSS-MF and molecular predictors: A comparative analysis of prognostic models for myelofibrosis

    HemaSphere

  3. The prognostic impact of non-driver gene mutations and variant allele frequency in primary myelofibrosis

    American Journal of Hematology

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

    British Journal of Haematology, Vol. 204, Núm. 4, pp. 1529-1535

2023

  1. A prognostic model based on gene expression parameters predicts a better response to bortezomib-containing immunochemotherapy in diffuse large B-cell lymphoma

    Frontiers in Oncology, Vol. 13

  2. Corrigendum: Evaluation of the Stellae-123 prognostic gene expression signature in acute myeloid leukemia(Front. Oncol., (2022), 12, (968340), 10.3389/fonc.2022.968340)

    Frontiers in Oncology

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

    HemaSphere, Vol. 7, Núm. 10, pp. E961

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

    HemaSphere, Vol. 7, Núm. 1, pp. E818

2022

  1. Erratum: Gene expression profiling identifies FLT3 mutation-like cases in wildtype FLT3 acute myeloid leukemia (PLoS ONE (2021) 16:2 (e0247093) DOI: 10.1371/journal.pone.0247093)

    PLoS ONE

  2. Evaluation of the Stellae-123 prognostic gene expression signature in acute myeloid leukemia

    Frontiers in Oncology, Vol. 12

  3. Prognostic Stratification of Diffuse Large B-cell Lymphoma Using Clinico-genomic Models: Validation and Improvement of the LymForest-25 Model

    HemaSphere, Vol. 6, Núm. 4

2021

  1. Detection of new drivers of frequent B-cell lymphoid neoplasms using an integrated analysis of whole genomes

    PloS one, Vol. 16, Núm. 5, pp. e0248886

  2. Gene expression profiling identifies FLT3 mutation-like cases in wild-type FLT3 acute myeloid leukemia

    PloS one, Vol. 16, Núm. 2, pp. e0247093

  3. Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models

    Frontiers in Oncology, Vol. 11

  4. Safety of FLT3 inhibitors in patients with acute myeloid leukemia

    Expert Review of Hematology, Vol. 14, Núm. 9, pp. 851-865

  5. Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data

    Leukemia, Vol. 35, Núm. 10, pp. 2924-2935

2020

  1. FLT3 inhibitors in the treatment of acute myeloid leukemia: Current status and future perspectives

    Minerva Medica, Vol. 111, Núm. 5, pp. 427-442

  2. Improved personalized survival prediction of patients with diffuse large B-cell Lymphoma using gene expression profiling

    BMC Cancer, Vol. 20, Núm. 1

2019

  1. The association of germline variants with chronic lymphocytic leukemia outcome suggests the implication of novel genes and pathways in clinical evolution

    BMC Cancer, Vol. 19, Núm. 1