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Simple and Scalable Algorithms for Cluster-Aware Precision Medicine.

Amanda M Buch1, Conor Liston1, Logan Grosenick1

  • 1Dept. of Psychiatry & BMRI, Weill Cornell Medicine, Cornell University.

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|July 17, 2024
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Summary
This summary is machine-generated.

This study introduces a novel cluster-aware embedding method for AI in precision medicine. It effectively identifies patient subgroups in complex biomedical data, outperforming existing approaches.

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Area of Science:

  • Computational biology
  • Biomedical informatics
  • Machine learning in healthcare

Background:

  • Biomedical data for AI in precision medicine is high-dimensional, clustered, and often has limited sample sizes.
  • Existing methods for joint embedding and clustering face complexity and limitations.
  • Identifying patient subgroups is crucial for personalized treatment strategies.

Purpose of the Study:

  • To develop a simple, scalable, and cluster-aware embedding approach for AI-driven precision medicine.
  • To overcome limitations of current joint embedding and clustering techniques.
  • To enable interpretable patient subgroup identification in multiomics and neuroimaging data.

Main Methods:

  • A modular approach combining latent factor methods with a convex clustering penalty.
  • Enables hierarchically clustered principal component analysis (PCA), locally linear embedding (LLE), and canonical correlation analysis (CCA).
  • Evaluated through numerical experiments and real-world multiomics and neuroimaging datasets.

Main Results:

  • The proposed method outperforms fourteen existing clustering methods on underdetermined and large sample datasets.
  • It does not require pre-specification of the number of clusters and improves model selection.
  • Generates interpretable hierarchically clustered embedding dendrograms.

Conclusions:

  • The novel cluster-aware embedding approach significantly improves patient subgroup identification for precision medicine.
  • It offers scalable and interpretable biomarkers by effectively handling complex biomedical data.
  • This method enhances AI-enabled healthcare outcomes through better data analysis.