% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Flther:643146,
      author       = {Flöther, Frederik F. and Blankenberg, Daniel and Demidik,
                      Maria and Jansen, Karl and Krishnakumar, Raga and
                      Krishnakumar, Rajiv and Laanait, Nouamane and Parida, Laxmi
                      and Saab, Carl Y. and Utro, Filippo},
      title        = {{H}ow quantum computing can enhance biomarker discovery},
      reportid     = {PUBDB-2026-00044, arXiv:2411.10511},
      year         = {2025},
      note         = {How quantum computing can enhance biomarker discovery.
                      Patterns (2025)},
      abstract     = {Biomarkers play a central role in medicine’s gradual
                      progress toward proactive, personalized precision
                      diagnostics and interventions. However, finding biomarkers
                      that provide very early indicators of a change in health
                      status, for example, for multifactorial diseases, has been
                      challenging. The 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, 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—multidimensional, time series, and
                      erroneous data—and covers key data modalities in
                      healthcare—electronic health records, omics, and medical
                      images. An outlook is provided concerning open research
                      challenges. Precision medicine is a lofty goal, and
                      challenges abound. A key ingredient to facilitate proactive
                      interventions that keep an individual healthy is the
                      detection of the earliest signals that the individual's
                      health status is changing. Identification of such biomarkers
                      requires advanced algorithms and analytics. Enter quantum
                      computing. While still an emerging technology, quantum
                      algorithms, particularly quantum machine learning, can
                      uncover patterns that classical techniques cannot. This
                      could enable the discovery of novel biomarkers and thus
                      accelerate progress toward precision medicine. The authors
                      discuss how quantum computing can improve biomarker
                      discovery. This perspective highlights the application of
                      quantum algorithms in analyzing complex healthcare data,
                      including electronic health records, omics, and medical
                      images, addresses the challenges of this technology, and
                      provides an outlook on open research challenges in this
                      field.},
      cin          = {CQTA},
      cid          = {I:(DE-H253)CQTA-20221102},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611) / QUEST -
                      QUantum computing for Excellence in Science and Technology
                      (101087126)},
      pid          = {G:(DE-HGF)POF4-611 / G:(EU-Grant)101087126},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2411.10511},
      howpublished = {arXiv:2411.10511},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2411.10511;\%\%$},
      doi          = {10.3204/PUBDB-2026-00044},
      url          = {https://bib-pubdb1.desy.de/record/643146},
}