How quantum computing can enhance biomarker discovery
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918007879499776 |
|---|---|
| author | Flöther, Frederik F. Blankenberg, Daniel Demidik, Maria Jansen, Karl Krishnakumar, Raga Krishnakumar, Rajiv Laanait, Nouamane Parida, Laxmi Saab, Carl Utro, Filippo |
| author_facet | Flöther, Frederik F. Blankenberg, Daniel Demidik, Maria Jansen, Karl Krishnakumar, Raga Krishnakumar, Rajiv Laanait, Nouamane Parida, Laxmi Saab, Carl Utro, Filippo |
| contents | Biomarkers 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, for example 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10511 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How quantum computing can enhance biomarker discovery Flöther, Frederik F. Blankenberg, Daniel Demidik, Maria Jansen, Karl Krishnakumar, Raga Krishnakumar, Rajiv Laanait, Nouamane Parida, Laxmi Saab, Carl Utro, Filippo Other Quantitative Biology Quantum Physics Biomarkers 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, for example 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. |
| title | How quantum computing can enhance biomarker discovery |
| topic | Other Quantitative Biology Quantum Physics |
| url | https://arxiv.org/abs/2411.10511 |