Publicacións en colaboración con investigadores/as de Complexo Hospitalario Universitario de Santiago (23)

2024

  1. Prognostic and survival factors in head and neck extra-nodal non-Hodgkin's lymphoma

    Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology

2023

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

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

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

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

2022

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

    Clinical cancer research : an official journal of the American Association for Cancer Research

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

    Frontiers in Oncology, Vol. 12

  3. Immune Checkpoint Inhibitors in Acute Myeloid Leukemia: A Meta-Analysis

    Frontiers in Oncology, Vol. 12

  4. Prognostic Stratification of Multiple Myeloma Using Clinicogenomic Models: Validation and Performance Analysis of the IAC-50 Model

    HemaSphere, Vol. 6, Núm. 8

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

    Blood cancer journal, Vol. 12, Núm. 4, pp. 76

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. Personalized Survival Prediction of Patients With Acute Myeloblastic Leukemia Using Gene Expression Profiling

    Frontiers in Oncology, Vol. 11

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

    Frontiers in Oncology, Vol. 11

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

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

  6. 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

  7. The safety profile of FLT3 inhibitors in the treatment of newly diagnosed or relapsed/refractory acute myeloid leukemia

    Expert Opinion on Drug Safety, Vol. 20, Núm. 7, pp. 791-799

  8. Variable expressivity and allelic heterogeneity in type 2 familial partial lipodystrophy: The p.(thr528met) lmna variant

    Journal of Clinical Medicine, Vol. 10, Núm. 7

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

  3. Novel Mutation Hotspots within Non-Coding Regulatory Regions of the Chronic Lymphocytic Leukemia Genome

    Scientific Reports, Vol. 10, Núm. 1

2019

  1. A Three-Gene Expression Signature Identifies a Cluster of Patients with Short Survival in Chronic Lymphocytic Leukemia

    Journal of Oncology, Vol. 2019