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Prioritizing Amyloid Imaging Biomarkers in Alzheimer’s Disease via Learning to Rank

  • Bo Peng
  • Zhiyun Ren
  • Xiaohui Yao
  • Kefei Liu
  • Andrew J. Saykin
  • Li Shen
  • Xia NingEmail author
  • for the ADNI
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11846)

Abstract

We propose an innovative machine learning paradigm enabling precision medicine for AD biomarker discovery. The paradigm tailors the imaging biomarker discovery process to individual characteristics of a given patient. We implement this paradigm using a newly developed learning-to-rank method \(\mathop {\mathtt {PLTR}}\limits \). The \(\mathop {\mathtt {PLTR}}\limits \) model seamlessly integrates two objectives for joint optimization: pushing up relevant biomarkers and ranking among relevant biomarkers. The empirical study of \(\mathop {\mathtt {PLTR}}\limits \) conducted on the ADNI data yields promising results to identify and prioritize individual-specific amyloid imaging biomarkers based on the individual’s structural MRI data. The resulting top ranked imaging biomarker has the potential to aid personalized diagnosis and disease subtyping.

Keywords

Amyloid PET Structural MRI Imaging biomarker prioritization Learning to rank Alzheimer’s disease 

Notes

Acknowledgements

This work was supported in part by NIH R01 EB022574, R01 LM011360, U19 AG024904, R01 AG019771, and P30 AG010133; NSF IIS 1837964 and 1855501. The complete ADNI Acknowledgement is available at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bo Peng
    • 1
  • Zhiyun Ren
    • 1
  • Xiaohui Yao
    • 2
  • Kefei Liu
    • 2
  • Andrew J. Saykin
    • 3
  • Li Shen
    • 2
  • Xia Ning
    • 1
    Email author
  • for the ADNI
  1. 1.The Ohio State UniversityColumbusUSA
  2. 2.University of PennsylvaniaPhiladelphiaUSA
  3. 3.Indiana University School of MedicineIndianapolisUSA

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