An artificial intelligence-assisted clinical framework to facilitate diagnostics and translational discovery in hematologic neoplasiaResearch in context

Summary: Background: The increasing volume and intricacy of sequencing data, along with other clinical and diagnostic data, like drug responses and measurable residual disease, creates challenges for efficient clinical comprehension and interpretation. Using paediatric B-cell precursor acute lympho...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Ming Tang, Željko Antić, Pedram Fardzadeh, Stefan Pietzsch, Charlotte Schröder, Adrian Eberhardt, Alena van Bömmel, Gabriele Escherich, Winfried Hofmann, Martin A. Horstmann, Thomas Illig, J. Matt McCrary, Jana Lentes, Markus Metzler, Wolfgang Nejdl, Brigitte Schlegelberger, Martin Schrappe, Martin Zimmermann, Karolina Miarka-Walczyk, Agata Patsorczak, Gunnar Cario, Bernhard Y. Renard, Martin Stanulla, Anke Katharina Bergmann
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	Elsevier 2024-06-01
Sarja:	EBioMedicine
Aiheet:	Clinical framework Data integration Machine learning Leukaemia
Linkit:	http://www.sciencedirect.com/science/article/pii/S2352396424002068
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