{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T10:55:11Z","timestamp":1790765711759,"version":"4.1.0"},"reference-count":63,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computational Biology and Chemistry"],"published-print":{"date-parts":[[2027,2]]},"DOI":"10.1016\/j.compbiolchem.2026.109408","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T23:58:17Z","timestamp":1789603097000},"page":"109408","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P1","title":["Global transfer learning pipeline for protein disease association in Alzheimer\u2019s disease"],"prefix":"10.1016","volume":"126","author":[{"given":"Hansa J.","family":"Thattil","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arunkumar","family":"M.N.","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.compbiolchem.2026.109408_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.jsb.2024.108118","article-title":"Studying protein\u2013protein interactions: Latest and most popular approaches","volume":"216","author":"Akbarzadeh","year":"2024","journal-title":"J. Struct. Biol."},{"issue":"20","key":"10.1016\/j.compbiolchem.2026.109408_b2","doi-asserted-by":"crossref","first-page":"11106","DOI":"10.3390\/ijms222011106","article-title":"The expression and localisation of GIRK channels is differentially altered in the hippocampus of two mouse models of Alzheimer\u2019s disease","volume":"22","author":"Alfaro-Ruiz","year":"2021","journal-title":"Int. J. Mol. Sci."},{"issue":"3","key":"10.1016\/j.compbiolchem.2026.109408_b3","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1093\/bioinformatics\/bti021","article-title":"Iterative cluster analysis of protein interaction data","volume":"21","author":"Arnau","year":"2005","journal-title":"Bioinformatics"},{"key":"10.1016\/j.compbiolchem.2026.109408_b4","series-title":"International Conference on Mathematics and Logics in Computer Science","first-page":"379","article-title":"Integrative gene prediction framework for Parkinson\u2019s disease using machine learning","author":"Awasthi","year":"2025"},{"key":"10.1016\/j.compbiolchem.2026.109408_b5","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/s13024-020-00384-6","article-title":"Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer\u2019s disease","volume":"15","author":"Bai","year":"2020","journal-title":"Mol. Neurodegener."},{"key":"10.1016\/j.compbiolchem.2026.109408_b6","series-title":"Multivariate GWAS of Alzheimer\u2019s disease CSF biomarker profiles implies GRIN2D in synaptic functioning","author":"Baranowska","year":"2022"},{"key":"10.1016\/j.compbiolchem.2026.109408_b7","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1016\/j.ins.2021.08.099","article-title":"A graph-based approach for positive and unlabeled learning","volume":"580","author":"Carnevali","year":"2021","journal-title":"Inform. Sci."},{"key":"10.1016\/j.compbiolchem.2026.109408_b8","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/978-1-0716-0826-5_3","article-title":"Siamese neural networks: An overview","author":"Chicco","year":"2021","journal-title":"Artif. Neural Netw."},{"issue":"12","key":"10.1016\/j.compbiolchem.2026.109408_b9","doi-asserted-by":"crossref","first-page":"2674","DOI":"10.3390\/math11122674","article-title":"Matrix factorization techniques in machine learning, signal processing, and statistics","volume":"11","author":"Du","year":"2023","journal-title":"Mathematics"},{"issue":"6","key":"10.1016\/j.compbiolchem.2026.109408_b10","first-page":"975","article-title":"Hyperparameter optimization in machine learning","volume":"18","author":"Franceschi","year":"2025","journal-title":"Found. Trends\u00ae Mach. Learn."},{"issue":"19","key":"10.1016\/j.compbiolchem.2026.109408_b11","doi-asserted-by":"crossref","first-page":"14189","DOI":"10.1007\/s00500-023-08390-8","article-title":"An integrated network topology and deep learning model for prediction of Alzheimer disease candidate genes","volume":"27","author":"Gnanadesigan","year":"2023","journal-title":"Soft Comput."},{"key":"10.1016\/j.compbiolchem.2026.109408_b12","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J., 2016. node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 855\u2013864.","DOI":"10.1145\/2939672.2939754"},{"issue":"5","key":"10.1016\/j.compbiolchem.2026.109408_b13","doi-asserted-by":"crossref","first-page":"3849","DOI":"10.1021\/acs.analchem.5c06080","article-title":"CRISPR-AD: Combinational detection of blood protein and miRNA with digital CRISPR-based assay enable to improve the diagnostic performance of Alzheimer\u2019s disease","volume":"98","author":"Guo","year":"2026","journal-title":"Anal. Chem."},{"issue":"3","key":"10.1016\/j.compbiolchem.2026.109408_b14","doi-asserted-by":"crossref","first-page":"536","DOI":"10.3390\/biomedicines13030536","article-title":"Deepwalk-based graph embeddings for mirna\u2013disease association prediction using deep neural network","volume":"13","author":"Ha","year":"2025","journal-title":"Biomedicines"},{"key":"10.1016\/j.compbiolchem.2026.109408_b15","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"4","key":"10.1016\/j.compbiolchem.2026.109408_b16","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad203","article-title":"Improving the identification of miRNA\u2013disease associations with multi-task learning on gene\u2013disease networks","volume":"24","author":"He","year":"2023","journal-title":"Brief. Bioinform."},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109408_b17","doi-asserted-by":"crossref","DOI":"10.1155\/2024\/4749668","article-title":"PU-GNN: A positive-unlabeled learning method for polypharmacy side-effects detection based on graph neural networks","volume":"2024","author":"Keshavarz","year":"2024","journal-title":"Int. J. Intell. Syst."},{"key":"10.1016\/j.compbiolchem.2026.109408_b18","article-title":"Positive-unlabeled learning with non-negative risk estimator","volume":"30","author":"Kiryo","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"9","key":"10.1016\/j.compbiolchem.2026.109408_b19","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btad545","article-title":"Automated machine learning for genome wide association studies","volume":"39","author":"Lakiotaki","year":"2023","journal-title":"Bioinformatics"},{"key":"10.1016\/j.compbiolchem.2026.109408_b20","article-title":"Predicting mutation-disease associations through protein interactions via deep learning","author":"Li","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109408_b21","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab461","article-title":"Positive-unlabeled learning in bioinformatics and computational biology: A brief review","volume":"23","author":"Li","year":"2022","journal-title":"Brief. Bioinform."},{"issue":"3","key":"10.1016\/j.compbiolchem.2026.109408_b22","doi-asserted-by":"crossref","first-page":"bbad118","DOI":"10.1093\/bib\/bbad118","article-title":"End-to-end interpretable disease\u2013gene association prediction","volume":"24","author":"Li","year":"2023","journal-title":"Brief. Bioinform."},{"issue":"6","key":"10.1016\/j.compbiolchem.2026.109408_b23","article-title":"AD-GCN: A novel graph convolutional network integrating multi-omics data for enhanced Alzheimer\u2019s disease diagnosis","volume":"20","author":"Li","year":"2025","journal-title":"PLoS One"},{"issue":"6","key":"10.1016\/j.compbiolchem.2026.109408_b24","first-page":"824","article-title":"Identification of disease-protein associations from a heterogeneous network using graph embedding and sample screening algorithms","volume":"21","author":"Li","year":"2024","journal-title":"Curr. Proteom."},{"key":"10.1016\/j.compbiolchem.2026.109408_b25","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.conb.2021.09.003","article-title":"Secretases in Alzheimer\u2019s disease: Novel insights into proteolysis of APP and TREM2","volume":"72","author":"Lichtenthaler","year":"2022","journal-title":"Curr. Opin. Neurobiol."},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109408_b26","doi-asserted-by":"crossref","first-page":"bbae611","DOI":"10.1093\/bib\/bbae611","article-title":"LIMO-GCN: A linear model-integrated graph convolutional network for predicting Alzheimer disease genes","volume":"26","author":"Lin","year":"2025","journal-title":"Brief. Bioinform."},{"issue":"19","key":"10.1016\/j.compbiolchem.2026.109408_b27","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1093\/bioinformatics\/btz155","article-title":"Enhancing the prediction of disease\u2013gene associations with multimodal deep learning","volume":"35","author":"Luo","year":"2019","journal-title":"Bioinformatics"},{"key":"10.1016\/j.compbiolchem.2026.109408_b28","first-page":"1808","article-title":"Ensemble learning for disease prediction: A review","volume":"vol. 11","author":"Mahajan","year":"2023"},{"issue":"5","key":"10.1016\/j.compbiolchem.2026.109408_b29","doi-asserted-by":"crossref","first-page":"bbae380","DOI":"10.1093\/bib\/bbae380","article-title":"Heterogeneous biomedical entity representation learning for gene\u2013disease association prediction","volume":"25","author":"Meng","year":"2024","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.compbiolchem.2026.109408_b30","series-title":"Overexpression of HIF-2\u03b1 in protected regions of Alzheimer\u2019s disease resilient cases","author":"Mitri","year":"2020"},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109408_b31","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1186\/s12920-025-02109-4","article-title":"Disease candidate genes prediction using positive labeled and unlabeled instances","volume":"18","author":"Molaei","year":"2025","journal-title":"BMC Med. Genom."},{"key":"10.1016\/j.compbiolchem.2026.109408_b32","doi-asserted-by":"crossref","unstructured":"Mou, L., Men, R., Li, G., Xu, Y., Zhang, L., Yan, R., Jin, Z., 2016. Natural language inference by tree-based convolution and heuristic matching. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). pp. 130\u2013136.","DOI":"10.18653\/v1\/P16-2022"},{"issue":"4","key":"10.1016\/j.compbiolchem.2026.109408_b33","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1177\/1533317513488925","article-title":"The neglected role of histamine in Alzheimer\u2019s disease","volume":"28","author":"Naddafi","year":"2013","journal-title":"Am. J. Alzheimer\u2019s Dis. Other Dementias"},{"issue":"2","key":"10.1016\/j.compbiolchem.2026.109408_b34","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad094","article-title":"AMHMDA: Attention aware multi-view similarity networks and hypergraph learning for miRNA\u2013disease associations identification","volume":"24","author":"Ning","year":"2023","journal-title":"Brief. Bioinform."},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109408_b35","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s13326-023-00291-x","article-title":"Multi-domain knowledge graph embeddings for gene-disease association prediction","volume":"14","author":"Nunes","year":"2023","journal-title":"J. Biomed. Semant."},{"issue":"17","key":"10.1016\/j.compbiolchem.2026.109408_b36","doi-asserted-by":"crossref","first-page":"2789","DOI":"10.3390\/electronics11172789","article-title":"Multi-class positive and unlabeled learning for high dimensional data based on outlier detection in a low dimensional embedding space","volume":"11","author":"Park","year":"2022","journal-title":"Electronics"},{"key":"10.1016\/j.compbiolchem.2026.109408_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108999","article-title":"Positive-Unlabelled learning for identifying new candidate Dietary Restriction-related genes among ageing-related genes","volume":"180","author":"Paz-Ruza","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiolchem.2026.109408_b38","doi-asserted-by":"crossref","DOI":"10.3389\/fmicb.2023.1244527","article-title":"Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network","volume":"14","author":"Peng","year":"2023","journal-title":"Front. Microbiol."},{"key":"10.1016\/j.compbiolchem.2026.109408_b39","first-page":"gkw943","article-title":"DisGeNET: A comprehensive platform integrating information on human disease-associated genes and variants","author":"Pi\u00f1ero","year":"2016","journal-title":"Nucleic Acids Res."},{"issue":"2","key":"10.1016\/j.compbiolchem.2026.109408_b40","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1002\/bies.201000044","article-title":"Protein-protein interactions: Making sense of networks via graph-theoretic modeling","volume":"33","author":"Pr\u017eulj","year":"2011","journal-title":"Bioessays"},{"key":"10.1016\/j.compbiolchem.2026.109408_b41","series-title":"Sentence-bert: Sentence embeddings using siamese bert-networks","author":"Reimers","year":"2019"},{"issue":"2","key":"10.1016\/j.compbiolchem.2026.109408_b42","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1590\/s1980-57642008dn10200002","article-title":"Targeting the gastrin-releasing peptide receptor pathway to treat cognitive dysfunction associated with Alzheimer\u2019s Disease","volume":"1","author":"Roesler","year":"2007","journal-title":"Dement. Neuropsychol."},{"issue":"4","key":"10.1016\/j.compbiolchem.2026.109408_b43","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.sbi.2005.06.001","article-title":"Protein\u2013protein interactions in human disease","volume":"15","author":"Ryan","year":"2005","journal-title":"Curr. Opin. Struct. Biol."},{"key":"10.1016\/j.compbiolchem.2026.109408_b44","doi-asserted-by":"crossref","first-page":"14658","DOI":"10.1038\/s41598-017-15306-8","article-title":"Activation of G-protein-gated inwardly rectifying potassium (Kir3\/GirK) channels rescues hippocampal functions in a mouse model of early amyloid-\u03b2 pathology","volume":"7","author":"Sanchez-Rodriguez","year":"2017","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compbiolchem.2026.109408_b45","doi-asserted-by":"crossref","unstructured":"Saunders, J.D., Freitas, A.A., 2022. Ga-auto-PU: A genetic algorithm-based automated machine learning system for positive-unlabeled learning. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. pp. 288\u2013291.","DOI":"10.1145\/3520304.3528932"},{"issue":"12","key":"10.1016\/j.compbiolchem.2026.109408_b46","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1007\/s10489-025-06706-9","article-title":"Automated machine learning for positive-unlabelled learning","volume":"55","author":"Saunders","year":"2025","journal-title":"Appl. Intell."},{"issue":"3","key":"10.1016\/j.compbiolchem.2026.109408_b47","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/TCBB.2024.3351752","article-title":"A survey of deep learning for detecting miRNA-disease associations: Databases, computational methods, challenges, and future directions","volume":"21","author":"Sheng","year":"2024","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"10.1016\/j.compbiolchem.2026.109408_b48","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2023.1222346","article-title":"DapBCH: A disease association prediction model Based on Cross-species and Heterogeneous graph embedding","volume":"14","author":"Shi","year":"2023","journal-title":"Front. Genet."},{"issue":"2","key":"10.1016\/j.compbiolchem.2026.109408_b49","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btac848","article-title":"NIAPU: Network-informed adaptive positive-unlabeled learning for disease gene identification","volume":"39","author":"Stolfi","year":"2023","journal-title":"Bioinformatics"},{"issue":"12","key":"10.1016\/j.compbiolchem.2026.109408_b50","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btaf580","article-title":"ADMGCN: Graph convolutional network for Alzheimer\u2019s disease diagnosis with a meta-learning paradigm","volume":"41","author":"Sun","year":"2025","journal-title":"Bioinformatics"},{"issue":"4","key":"10.1016\/j.compbiolchem.2026.109408_b51","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac266","article-title":"A deep learning method for predicting metabolite\u2013disease associations via graph neural network","volume":"23","author":"Sun","year":"2022","journal-title":"Brief. Bioinform."},{"issue":"15","key":"10.1016\/j.compbiolchem.2026.109408_b52","doi-asserted-by":"crossref","first-page":"8980","DOI":"10.3390\/app13158980","article-title":"Gene association classification for autism spectrum disorder: Leveraging gene embedding and differential gene expression profiles to identify disease-related genes","volume":"13","author":"Suratanee","year":"2023","journal-title":"Appl. Sci."},{"issue":"D1","key":"10.1016\/j.compbiolchem.2026.109408_b53","doi-asserted-by":"crossref","first-page":"D638","DOI":"10.1093\/nar\/gkac1000","article-title":"The STRING database in 2023: protein\u2013protein association networks and functional enrichment analyses for any sequenced genome of interest","volume":"51","author":"Szklarczyk","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.compbiolchem.2026.109408_b54","doi-asserted-by":"crossref","DOI":"10.1016\/j.jmgm.2023.108511","article-title":"Parameterization and validation of a new AMBER force field for an oxovanadium (IV) complex with therapeutic potential implications in Alzheimer\u2019s disease","volume":"122","author":"Tavares","year":"2023","journal-title":"J. Mol. Graph. Model."},{"key":"10.1016\/j.compbiolchem.2026.109408_b55","article-title":"Identification of early Alzheimer\u2019s disease subclass and signature genes based on PANoptosis genes","volume":"17","author":"Wang","year":"2024","journal-title":"Front. Mol. Neurosci."},{"issue":"6","key":"10.1016\/j.compbiolchem.2026.109408_b56","doi-asserted-by":"crossref","first-page":"bvaf056","DOI":"10.1210\/jendso\/bvaf056","article-title":"The glucagon receptor is expressed in the frontal cortex and impaired signaling associates with cognitive decline","volume":"9","author":"Winther","year":"2025","journal-title":"J. Endocr. Soc."},{"issue":"9","key":"10.1016\/j.compbiolchem.2026.109408_b57","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1038\/s41587-024-02428-4","article-title":"A structurally informed human protein\u2013protein interactome reveals proteome-wide perturbations caused by disease mutations","volume":"43","author":"Xiong","year":"2025","journal-title":"Nature Biotechnol."},{"issue":"4","key":"10.1016\/j.compbiolchem.2026.109408_b58","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1016\/S0306-4522(00)00230-X","article-title":"Histamine H1 receptors in patients with Alzheimer\u2019s disease assessed by positron emission tomography","volume":"99","author":"Yanai","year":"2000","journal-title":"Neuroscience"},{"key":"10.1016\/j.compbiolchem.2026.109408_b59","series-title":"2019 IEEE International Parallel and Distributed Processing Symposium","first-page":"462","article-title":"Accurate, efficient and scalable graph embedding","author":"Zeng","year":"2019"},{"issue":"Suppl 6","key":"10.1016\/j.compbiolchem.2026.109408_b60","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1186\/s12864-024-11117-0","article-title":"Protein structural domain-disease association prediction based on heterogeneous networks","volume":"23","author":"Zhang","year":"2025","journal-title":"BMC Genomics"},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109408_b61","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/1756-6606-4-3","article-title":"APP processing in Alzheimer\u2019s disease","volume":"4","author":"Zhang","year":"2011","journal-title":"Mol. Brain"},{"key":"10.1016\/j.compbiolchem.2026.109408_b62","first-page":"4123","article-title":"Proteomic and transcriptomic analyses reveal pathological changes in the entorhinal cortex region that correlate well with dysregulation of ion transport in patients with Alzheimer\u2019s disease","volume":"58","author":"Zhang","year":"2021","journal-title":"Mol. Neurobiol."},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109408_b63","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s13195-023-01378-4","article-title":"Associations between genetically predicted plasma protein levels and alzheimer\u2019s disease risk: A study using genetic prediction models","volume":"16","author":"Zhu","year":"2024","journal-title":"Alzheimer\u2019s Res. Ther."}],"container-title":["Computational Biology and Chemistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1476927126005359?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1476927126005359?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T09:56:11Z","timestamp":1790762171000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1476927126005359"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,2]]},"references-count":63,"alternative-id":["S1476927126005359"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiolchem.2026.109408","relation":{},"ISSN":["1476-9271"],"issn-type":[{"value":"1476-9271","type":"print"}],"subject":[],"published":{"date-parts":[[2027,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Global transfer learning pipeline for protein disease association in Alzheimer\u2019s disease","name":"articletitle","label":"Article Title"},{"value":"Computational Biology and Chemistry","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiolchem.2026.109408","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109408"}}