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000619365 1001_ $$0P:(DE-HGF)0$$aFlöther, Frederik F.$$b0
000619365 245__ $$aHow quantum computing can enhance biomarker discovery for multi-factorial diseases
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000619365 500__ $$aHow quantum computing can enhance biomarker discovery. Patterns (2025)
000619365 520__ $$aBiomarkers play a central role in medicine's gradual progress towards proactive, personalized precision diagnostics and interventions. However, finding biomarkers that provide very early indicators of a change in health status, particularly for multi-factorial diseases, has been challenging. Discovery of such biomarkers stands to benefit significantly from advanced information processing and means to detect complex correlations, which quantum computing offers. In this perspective paper, quantum algorithms, particularly in machine learning, are mapped to key applications in biomarker discovery. The opportunities and challenges associated with the algorithms and applications are discussed. The analysis is structured according to different data types - multi-dimensional, time series, and erroneous data - and covers key data modalities in healthcare - electronic health records (EHRs), omics, and medical images. An outlook is provided concerning open research challenges.
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000619365 7001_ $$0P:(DE-HGF)0$$aBlankenberg, Daniel$$b1
000619365 7001_ $$0P:(DE-H253)PIP1101939$$aDemidik, Maria$$b2$$eCorresponding author
000619365 7001_ $$0P:(DE-H253)PIP1003636$$aJansen, Karl$$b3
000619365 7001_ $$0P:(DE-HGF)0$$aKrishnakumar, Raga$$b4
000619365 7001_ $$0P:(DE-HGF)0$$aKrishnakumar, Rajiv$$b5
000619365 7001_ $$0P:(DE-HGF)0$$aLaanait, Nouamane$$b6
000619365 7001_ $$0P:(DE-HGF)0$$aParida, Laxmi$$b7
000619365 7001_ $$0P:(DE-HGF)0$$aSaab, Carl$$b8
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000619365 7870_ $$0PUBDB-2026-00044$$aFlöther, Frederik F. et.al.$$d2025$$iIsParent$$rarXiv:2411.10511$$tHow quantum computing can enhance biomarker discovery
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