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The unsupervised learning rank aggregation algorithm from SemNet 2.0 compared the most important amino acid, peptide, and protein (AAPP) nodes connected to AD, ALS, or FTD. FTD shared 99.9% of its nodes with ALS and AD; AD shared 64.2% of its nodes with FTD and ALS; and ALS shared 68.3% of its nodes with AD and FTD. The results were validated and mapped to functional biological processes using supervised human supervision and an external large language model. The overall percentages of mapped intersecting biological processes were as follows: inflammation and immune response, 19%; synapse and neurotransmission, 19%; cell cycle, 15%; protein aggregation, 12%; membrane regulation, 11%; stress response and regulation, 9%; and gene regulation, 4%. Once normalized for node count, biological mappings for cell cycle regulation and stress response were more prominent in the intersection of AD and FTD. Protein aggregation, gene regulation, and energetics were more prominent in the intersection of ALS and FTD. Synapse and neurotransmission, membrane regulation, and inflammation and immune response were greater at the intersection of AD and ALS. Given the extensive molecular pathophysiology overlap, small differences in regulation, genetic, or environmental factors likely shape the underlying expressed disease phenotype. The results help prioritize testable hypotheses for future clinical or experimental research.<\/jats:p>","DOI":"10.3390\/ijms252413450","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T04:14:52Z","timestamp":1734322492000},"page":"13450","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Artificial Intelligence-Assisted Comparative Analysis of the Overlapping Molecular Pathophysiology of Alzheimer\u2019s Disease, Amyotrophic Lateral Sclerosis, and Frontotemporal Dementia"],"prefix":"10.3390","volume":"25","author":[{"given":"Zihan","family":"Wei","sequence":"first","affiliation":[{"name":"Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology & Emory University School of Medicine, Atlanta, GA 30322, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meghna R.","family":"Iyer","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology & Emory University School of Medicine, Atlanta, GA 30322, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3039-3235","authenticated-orcid":false,"given":"Benjamin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology & Emory University School of Medicine, Atlanta, GA 30322, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"Deng","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology & Emory University School of Medicine, Atlanta, GA 30322, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5472-6355","authenticated-orcid":false,"given":"Cassie S.","family":"Mitchell","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology & Emory University School of Medicine, Atlanta, GA 30322, USA"},{"name":"Center for Machine Learning at Georgia Tech, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Risacher, S.L., and Saykin, A.J. 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