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Recently, plasma protein biomarkers have emerged as a cost-effective and less invasive tool for identifying neuropathological features, enhanced by machine learning (ML) for precise diagnosis. However, most ML studies fail to account for protein\u2013protein interactions (PPIs) and synergetic effects between proteins, overlooking their collective contributions to disease mechanisms. Additionally, the lack of consideration for functional properties may result in the redundant and imbalanced representation of proteins and their functions, potentially limiting the effectiveness of dementia diagnosis. In this study, we propose NeuroFANN, a method designed to classify three neuropathological subtypes in dementia\u2014positivity for A\u03b2, MTA, and WMH\u2014using plasma protein biomarkers. A key feature of NeuroFANN is the combination of the PPI network-based synergetic effects with the functional annotation-based protein biomarker clustering. NeuroFANN extracts synergetic effects by propagating independent effects of proteins across the PPI network, which are then aggregated in functional protein clusters, thereby enabling global PPI awareness and capturing the biological properties of protein biomarkers. From a South Korean cohort, 54 proteins were identified as plasma protein biomarkers for dementia subtypes and grouped into 16 clusters. NeuroFANN outperformed comparison methods in classifying dementia subtypes, with its core components validated as key contributors to superior performance. Additionally, the risk scores predicted by NeuroFANN showed a strong association with longitudinal cognitive decline, demonstrating its potential as a valuable diagnostic tool in clinical settings.<\/jats:p>","DOI":"10.1093\/bib\/bbaf366","type":"journal-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T13:55:08Z","timestamp":1754056508000},"source":"Crossref","is-referenced-by-count":0,"title":["NeuroFANN: identification of neuropathological subtypes in dementia with plasma proteins by using functionally annotated neural network"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5158-4670","authenticated-orcid":false,"given":"Sunghong","family":"Park","sequence":"first","affiliation":[{"name":"Department of Physiology, Ajou University School of Medicine , Worldcup-ro 164, Yeongtong-gu, Suwon, 16499 ,","place":["Republic of 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Psychiatry, Ajou University School of Medicine , Worldcup-ro 164, Yeongtong-gu, Suwon, 16499 ,","place":["Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyun Woong","family":"Roh","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Ajou University School of Medicine , Worldcup-ro 164, Yeongtong-gu, Suwon, 16499 ,","place":["Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8347-8277","authenticated-orcid":false,"given":"Hyunjung","family":"Shin","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Ajou University , Worldcup-ro 206, Yeongtong-gu, Suwon, 16499 ,","place":["Republic of Korea"]},{"name":"Department of Artificial Intelligence, Ajou University , Worldcup-ro 206, Yeongtong-gu, Suwon, 16499 ,","place":["Republic of 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