Journal
LAB ON A CHIP
Volume 18, Issue 3, Pages 395-405Publisher
ROYAL SOC CHEMISTRY
DOI: 10.1039/c7lc00955k
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Funding
- American Cancer Society - CEOs Against Cancer - CA Division Research Scholar Grant [RSG-15-227-01-CSM]
- Hartwell Foundation
- NIH [R21 5R21CA182336, 1 UH2 NS095495-01, 5R01NS099348]
- Mirowski Family Foundation
- Neil and Barbara Smit
- NATIONAL CANCER INSTITUTE [R33CA206907, R21CA182336] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE [UH2NS095495, R01NS099348] Funding Source: NIH RePORTER
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New technologies that measure sparse molecular biomarkers from easily accessible bodily fluids (e.g. blood, urine, and saliva) are revolutionizing disease diagnostics and precision medicine. Microchip devices can measure more disease biomarkers with better sensitivity and specificity each year, but clinical interpretation of these biomarkers remains a challenge. Single biomarkers in 'liquid biopsy' often cannot accurately predict the state of a disease due to heterogeneity in phenotype and disease expression across individuals. To address this challenge, investigators are combining multiplexed measurements of different biomarkers that together define robust signatures for specific disease states. Machine learning is a useful tool to automatically discover and detect these signatures, especially as new technologies output increasing quantities of molecular data. In this paper, we review the state of the field of machine learning applied to molecular diagnostics and provide practical guidance to use this tool effectively and to avoid common pitfalls.
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