{"doi":"10.1101/2024.09.25.614862","title":"WIMOAD: Weighted Integration of Multi-Omics Data with Meta Learning for Alzheimer’s Disease Diagnosis","abstract":"<jats:title>Abstract</jats:title>\n                <jats:sec>\n                  <jats:title>INTRODUCTION</jats:title>\n                  <jats:p>Alzheimer’s disease (AD), the most prevalent subtype of dementia, is characterized by a gradual decline in brain cognitive function. Early detection is critical for initiating timely interventions that may delay the severe progression of the disease. Recent advances in next-generation sequencing (NGS) offer promising, non-invasive, and cost-effective strategies for AD screening. However, most current approaches rely on single-omics data, which may fail to capture the complex biological heterogeneity among individuals.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>METHODS</jats:title>\n                  <jats:p>We introduce WIMOAD, a stacking ensemble and weighted multi-omics integration for AD diagnosis. It leverages paired gene expression and methylation data from ADNI and presents a meta learning framework for multi-cognitive stage classification during AD progression.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>RESULTS AND DISCUSSION</jats:title>\n                  <jats:p>WIMOAD outperforms existing integration methods in AD diagnosis, effectively capturing complex multi-omics patterns linked to clinical outcomes. Its interpretability also facilitates the detection of novel biomarkers across different omics layers.</jats:p>\n                </jats:sec>","journal":null,"year":null,"id":672638,"datarank":0.26876392038420827,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.0,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":301389,"name":"Jieqiong Wang","orcid":"0000-0002-7339-1626","position":1,"is_corresponding":false},{"id":258822,"name":"Shibiao Wan","orcid":"0000-0003-0661-2684","position":2,"is_corresponding":false},{"id":1224754,"name":"Hanyu Xiao","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"WIMOAD: Weighted Integration of Multi-Omics Data with Meta Learning for Alzheimer’s Disease Diagnosis","abstract":"<jats:title>Abstract</jats:title>\n                <jats:sec>\n                  <jats:title>INTRODUCTION</jats:title>\n                  <jats:p>Alzheimer’s disease (AD), the most prevalent subtype of dementia, is characterized by a gradual decline in brain cognitive function. Early detection is critical for initiating timely interventions that may delay the severe progression of the disease. Recent advances in next-generation sequencing (NGS) offer promising, non-invasive, and cost-effective strategies for AD screening. However, most current approaches rely on single-omics data, which may fail to capture the complex biological heterogeneity among individuals.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>METHODS</jats:title>\n                  <jats:p>We introduce WIMOAD, a stacking ensemble and weighted multi-omics integration for AD diagnosis. It leverages paired gene expression and methylation data from ADNI and presents a meta learning framework for multi-cognitive stage classification during AD progression.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>RESULTS AND DISCUSSION</jats:title>\n                  <jats:p>WIMOAD outperforms existing integration methods in AD diagnosis, effectively capturing complex multi-omics patterns linked to clinical outcomes. Its interpretability also facilitates the detection of novel biomarkers across different omics layers.</jats:p>\n                </jats:sec>","is_dataset_classified":null,"base_score":1.791759469228055,"endowment":1.791759469228055,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"39386613","pmcid":null,"openalex_id":"https://openalex.org/W4402918680","authors":[],"funders":[{"funder_name":"National Institutes of Health","grant_id":"3P20GM103427-23S2","title":"Nebraska Research Network in Functional Genomics"},{"funder_name":"National Institutes of Health","grant_id":"3P20GM130447-02S1","title":"Cognitive Neuroscience of Development and Aging (CoNDA) Center Supplement"},{"funder_name":"National Institutes of Health","grant_id":"5P30CA036727-23","title":"CORE--Confocal Microscopy"}],"total_grants":3,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2025,"count":5}],"oa_status":"green","license":"cc-by-nc-nd","oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2025/04/09/2024.09.25.614862.full.pdf","host_type":"repository"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2025/04/09/2024.09.25.614862.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2024.09.25.614862","host_type":"publisher"},{"url":"https://doi.org/10.1101/2024.09.25.614862","host_type":"repository"},{"url":"https://pubmed.ncbi.nlm.nih.gov/39386613","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11463407","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11463407/pdf/nihpp-2024.09.25.614862v2.pdf","host_type":"repository"}],"fields_of_study":["Bioinformatics and Genomic Networks","Alzheimer's disease research and treatments","Genetic Associations and Epidemiology","0202 electrical engineering, electronic engineering, information engineering","02 engineering and technology"],"mesh_terms":[],"keywords":["Omics","Dementia","Disease","Neuroimaging","Computer science","Cognitive decline","Machine learning","Cognition","Computational biology","Artificial intelligence","Bioinformatics","Medicine","Biology","Neuroscience","Pathology"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Peace, Justice and strong institutions"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-16T10:04:56.076756Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}