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Leveraging untargeted metabolomics in combination with machine learning to uncover novel insights into bladder cancer

Abstract Background Untargeted metabolomics has emerged as a powerful approach to uncover metabolic dysregulation associated with cancer progression. When integrated with a machine learning strategy it facilitates the discovery of key metabolic pathways and predictive biomarkers with high diagnostic...

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Principais autores: Abu Hena Mostafa Kamal, Vasanta Putluri, Tanmay Gandhi, Chandra Shekar R. Ambati, Chandra Sekhar Amara, Karthik Reddy Kami Reddy, Meredith Lauren Spradlin, Amrit Koirala, Sachin B. Jorvekar, Sandra L. Grimm, Dexue Fu, Krishna Parsawar, Felice de Jong, Chris Beecher, Subrata Sen, Seth P. Lerner, M. Minhaj Siddiqui, Yair Lotan, Livia S. Eberlin, Arun Sreekumar, Cristian Coarfa, Nagireddy Putluri
Format: Artigo
Sprog:Inglês
Udgivet: BMC 2026-04-01
Serier:Cancer & Metabolism
Fag:
Untargeted metabolomics
Orbitrap IQ-X
Bladder cancer
Machine learning
Online adgang:https://doi.org/10.1186/s40170-026-00427-4
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