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A Goemans-Williamson type algorithm for identifying subcohorts in clinical trials

arXiv — cs.LG•Tuesday, November 25, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
  • An efficient algorithm has been developed to identify predominantly homogeneous subcohorts of patients from large, inhomogeneous datasets, inspired by the Goemans-Williamson rounding technique. This algorithm has been applied to the RNA microarray dataset for breast cancer, revealing a potential link between LXR over-expression and BRCA2 and MSH6 methylation levels in specific patient subcohorts.
  • This advancement is significant as it enhances the ability to systematically identify clinically relevant patient subcohorts, which can lead to more tailored and effective treatment strategies in breast cancer research and clinical trials.
  • The development of this algorithm aligns with ongoing efforts to improve cancer detection and treatment through advanced data analysis techniques, emphasizing the importance of integrating diverse datasets and predictive models to address the complexities of cancer biology and patient care.
— via World Pulse Now AI Editorial System
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