Publicacións nas que colabora con José Díaz Arias (20)

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

2022

  1. Erratum: Correction: Gene expression profiling identifies FLT3 mutation-like cases in wild-type FLT3 acute myeloid leukemia (PloS one (2021) 16 2 (e0247093))

    PloS one

  2. 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

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

    Frontiers in Oncology, Vol. 12

  4. 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

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

    HemaSphere, Vol. 6, Núm. 8

  6. 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. 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

  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

  2. Identification of new putative driver mutations and predictors of disease evolution in chronic lymphocytic leukemia

    Blood cancer journal

  3. New Recurrent Structural Aberrations in the Genome of Chronic Lymphocytic Leukemia Based on Exome-Sequencing Data

    Frontiers in Genetics, Vol. 10

  4. 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