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Showing 1–8 of 8 results
Advanced filters: Author: Tabea Erdmann Clear advanced filters

  • Artificial intelligence (AI) system is known to improve dermatologists’ diagnostic accuracy for melanoma. This group applies the eye-tracking technology on dermatologists when diagnosing dermoscopic images of melanomas and reports improved balanced diagnostic accuracy when using an X(explainable) AI system comparing to the standard one.

    • Tirtha Chanda
    • Sarah Haggenmueller
    • Titus J. Brinker
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-10

  • Artificial intelligence has become popular as a cancer classification tool, but there is distrust of such systems due to their lack of transparency. Here, the authors develop an explainable AI system which produces text- and region-based explanations alongside its classifications which was assessed using clinicians’ diagnostic accuracy, diagnostic confidence, and their trust in the system.

    • Tirtha Chanda
    • Katja Hauser
    • Titus J. Brinker
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-17

  • Plasmablastic lymphoma (PBL) is an aggressive lymphoma subtype characterized by poor prognosis but the molecular knowledge of the disease is limited. Here, the authors perform whole exome sequencing and copy number determination of primary samples highlighting IRF4 and JAK-STAT pathways as therapeutic targets for PBL.

    • Fabian Frontzek
    • Annette M. Staiger
    • Georg Lenz
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-14

  • Neutrophils rapidly respond to bacterial and fungal infections but can cause substantial collateral tissue damage if not restrained. Rosenbauer and colleagues show that the transcription factor PU.1 serves a cell-intrinsic role to prevent over-exuberant neutrophil responses to fungal infection.

    • Josephine Fischer
    • Carolin Walter
    • Frank Rosenbauer
    Research
    Nature Immunology
    Volume: 20, P: 546-558

  • Chromatin conformation studies are limited by the large amounts of starting material required to perform current protocols. Here the authors present Low-C, a Hi-C method for low amounts of input material and produce Low-C maps from primary B-cells of a diffuse large B-cell lymphoma patient, demonstrating the suitability of Low-C to analyse rare cell populations.

    • Noelia Díaz
    • Kai Kruse
    • Juan M. Vaquerizas
    ResearchOpen Access
    Nature Communications
    Volume: 9, P: 1-13