Identification of molecular subtypes of dementia by using blood-proteins interaction-aware graph propagational network

Author:

Park Sunghong1ORCID,Hong Chang Hyung2,Son Sang Joon2,Roh Hyun Woong2,Kim Doyoon134,Shin Hyunjung56,Woo Hyun Goo13478ORCID

Affiliation:

1. Department of Physiology, Ajou University School of Medicine , Worldcup-ro 164, Yeongtong-gu, Suwon, 16499 , Republic of Korea

2. Department of Psychiatry, Ajou University School of Medicine , Woldcup-ro 164, Yeongtong-gu, Suwon, 16499 , Republic of Korea

3. Department of Biomedical Science , Graduate School, , Worldcup-ro 164, Yeongtong-gu, Suwon, 16499 , Republic of Korea

4. Ajou University , Graduate School, , Worldcup-ro 164, Yeongtong-gu, Suwon, 16499 , Republic of Korea

5. Department of Industrial Engineering, Ajou University , Worldcup-ro 206, Yeongtong-gu, Suwon, 16499 , Republic of Korea

6. Department of Artificial Intelligence, Ajou University , Worldcup-ro 206, Yeongtong-gu, Suwon, 16499 , Republic of Korea

7. Ajou Translational Omics Center (ATOC) , Research Institute for Innovative Medicine, , Worldcup-ro 164, Yeongtong-gu, Suwon, 16499 , Republic of Korea

8. Ajou University Medical Center , Research Institute for Innovative Medicine, , Worldcup-ro 164, Yeongtong-gu, Suwon, 16499 , Republic of Korea

Abstract

Abstract Plasma protein biomarkers have been considered promising tools for diagnosing dementia subtypes due to their low variability, cost-effectiveness, and minimal invasiveness in diagnostic procedures. Machine learning (ML) methods have been applied to enhance accuracy of the biomarker discovery. However, previous ML-based studies often overlook interactions between proteins, which are crucial in complex disorders like dementia. While protein–protein interactions (PPIs) have been used in network models, these models often fail to fully capture the diverse properties of PPIs due to their local awareness. This drawback increases the chance of neglecting critical components and magnifying the impact of noisy interactions. In this study, we propose a novel graph-based ML model for dementia subtype diagnosis, the graph propagational network (GPN). By propagating the independent effect of plasma proteins on PPI network, the GPN extracts the globally interactive effects between proteins. Experimental results showed that the interactive effect between proteins yielded to further clarify the differences between dementia subtype groups and contributed to the performance improvement where the GPN outperformed existing methods by 10.4% on average.

Funder

Korea government

Ministry of Health and Welfare (MOHW), Republic of Korea

Ministry of Science and ICT (MSIT), Republic of Korea

Ministry of Education (MOE), Republic of Korea

Korea Disease Control and Prevention Agency

Publisher

Oxford University Press (OUP)

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