{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhang J"],"funding":["National Natural Science Foundation of China"],"pagination":["btaf629"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12758602"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["42(1)"],"pubmed_abstract":["<h4>Motivation</h4>Neurodegenerative disorders influence millions of people worldwide, and uncovering the pathogenesis is of urgent need. Many efforts have been made to detect or predict neurodegenerative disorders, while exploring the pathogenesis has been ignored from a systemic perspective.<h4>Results</h4>To handle this issue, we propose a novel and powerful method, referred to as Pathogenesis-aware Mutual-Assistance Classification and Regression Optimization (Pa-MACRO). First, Pa-MACRO incorporates a mutual-assistance bidirectional mapping technique with a joint-embedding fine-grained interpretability module. This can extract the intrinsic factors and their interactions of multimodal pathogenesis. Second, our method can simultaneously classify an at-risk individual and predict the seve"],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["Mutual learning for joint disease detection and severity prediction reveals multimodal pathogenesis for neurodegenerative disorders."],"pmcid":["PMC12758602"],"funding_grant_id":["62136004","62373306"],"pubmed_authors":["Schulze E","Haviari A","Ayers K","Brewer J","Palou M","Ayres M","Rosen HJ","Yurko E","Riddle M","Montine T","Ramirez J","Manning S","Russell H","Blanchard K","Wint D","Matoush L","DeStefano S","Reeder S","Moore-Weiss J","Bailey R","Levey A","Wu CK","Fletcher E","Lee EB","Munic D","White M","Yu M","Schwarz C","Pietras A","May J","Ton A","Greig-Custo M","Turner RS","Ashe L","Cornell G","Villar M","Mossa D","Faircloth J","Scanlan M","Ferng N","Santiago S","Gallagher L","Petrocca E","Hosein C","Lowery S","Devis Y","Vincent S","Nyborg G","Becerra M","Chiotis K","Zimmerman C","Little E","Ferman D","Jo T","Apostolova 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J"],"additional_accession":[]},"is_claimable":false,"name":"Mutual learning for joint disease detection and severity prediction reveals multimodal pathogenesis for neurodegenerative disorders.","description":"<h4>Motivation</h4>Neurodegenerative disorders influence millions of people worldwide, and uncovering the pathogenesis is of urgent need. Many efforts have been made to detect or predict neurodegenerative disorders, while exploring the pathogenesis has been ignored from a systemic perspective.<h4>Results</h4>To handle this issue, we propose a novel and powerful method, referred to as Pathogenesis-aware Mutual-Assistance Classification and Regression Optimization (Pa-MACRO). First, Pa-MACRO incorporates a mutual-assistance bidirectional mapping technique with a joint-embedding fine-grained interpretability module. This can extract the intrinsic factors and their interactions of multimodal pathogenesis. Second, our method can simultaneously classify an at-risk individual and predict the seve","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Jan","modification":"2026-06-06T10:30:08.906Z","creation":"2026-05-29T03:11:23.951Z"},"accession":"S-EPMC12758602","cross_references":{"pubmed":["41452744"],"doi":["10.1093/bioinformatics/btaf629"]}}