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...

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Bibliografski detalji

Glavni autori:	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
Format:	Artigo
Jezik:	Inglês
Izdano:	Elsevier 2024-06-01
Serija:	EBioMedicine
Teme:	Clinical framework Data integration Machine learning Leukaemia
Online pristup:	http://www.sciencedirect.com/science/article/pii/S2352396424002068
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