{"doi":"10.1101/2024.08.01.606222","title":"mosGraphGPT: a foundation model for multi-omic signaling graphs using generative AI","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>\n                  Generative pretrained models represent a significant advancement in natural language processing and computer vision, which can generate coherent and contextually relevant content based on the pre-training on large general datasets and fine-tune for specific tasks. Building foundation models using large scale omic data is promising to decode and understand the complex signaling language patterns within cells. Different from existing foundation models of omic data, we build a foundation model,\n                  <jats:italic>mosGraphGPT</jats:italic>\n                  , for multi-omic signaling (mos) graphs, in which the multi-omic data was integrated and interpreted using a multi-level signaling graph. The model was pretrained using multi-omic data of cancers in The Cancer Genome Atlas (TCGA), and fine-turned for multi-omic data of Alzheimer’s Disease (AD). The experimental evaluation results showed that the model can not only improve the disease classification accuracy, but also is interpretable by uncovering disease targets and signaling interactions. And the model code are uploaded via GitHub with link:\n                  <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://github.com/mosGraph/mosGraphGPT\">https://github.com/mosGraph/mosGraphGPT</jats:ext-link>\n                </jats:p>","journal":null,"year":null,"id":651923,"datarank":0.3596842909197557,"base_score":2.3978952727983707,"endowment":2.3978952727983707,"self_citation_contribution":0.3596842909197557,"citation_network_contribution":0.0,"self_endowment_contribution":0.3596842909197557,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":10,"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":459227,"name":"Di Huang","orcid":"0000-0002-6809-6470","position":1,"is_corresponding":false},{"id":329380,"name":"Emily Chen","orcid":"0000-0001-7169-8192","position":2,"is_corresponding":false},{"id":1700427,"name":"Dekang Cao","orcid":null,"position":3,"is_corresponding":false},{"id":1700428,"name":"Tim Xu","orcid":null,"position":4,"is_corresponding":false},{"id":1700429,"name":"Ben Dizdar","orcid":null,"position":5,"is_corresponding":false},{"id":482516,"name":"Guangfu Li","orcid":"0000-0002-9817-568X","position":6,"is_corresponding":false},{"id":1178584,"name":"Yixin Chen","orcid":"0000-0002-9768-3164","position":7,"is_corresponding":false},{"id":225274,"name":"Philip Payne","orcid":"0000-0002-9532-2998","position":8,"is_corresponding":false},{"id":458272,"name":"Michael Province","orcid":null,"position":9,"is_corresponding":false},{"id":368999,"name":"Fuhai Li","orcid":"0000-0002-3773-146X","position":10,"is_corresponding":false},{"id":479653,"name":"Heming Zhang","orcid":"0000-0002-3171-5805","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"mosGraphGPT: a foundation model for multi-omic signaling graphs using generative AI","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>\n                  Generative pretrained models represent a significant advancement in natural language processing and computer vision, which can generate coherent and contextually relevant content based on the pre-training on large general datasets and fine-tune for specific tasks. Building foundation models using large scale omic data is promising to decode and understand the complex signaling language patterns within cells. Different from existing foundation models of omic data, we build a foundation model,\n                  <jats:italic>mosGraphGPT</jats:italic>\n                  , for multi-omic signaling (mos) graphs, in which the multi-omic data was integrated and interpreted using a multi-level signaling graph. The model was pretrained using multi-omic data of cancers in The Cancer Genome Atlas (TCGA), and fine-turned for multi-omic data of Alzheimer’s Disease (AD). The experimental evaluation results showed that the model can not only improve the disease classification accuracy, but also is interpretable by uncovering disease targets and signaling interactions. And the model code are uploaded via GitHub with link:\n                  <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://github.com/mosGraph/mosGraphGPT\">https://github.com/mosGraph/mosGraphGPT</jats:ext-link>\n                </jats:p>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19965766","pmcid":null,"openalex_id":null,"authors":[],"funders":[{"funder_name":"National Institutes of Health","grant_id":"1RM1NS132962-01","title":"Systems-Level Approach to Neuronopathic Lysosomal Storage Disorders"},{"funder_name":"National Institutes of Health","grant_id":"1R01LM013902-01A1","title":"Modeling and targeting tumor-immune signaling interactions in tumor microenvironment"},{"funder_name":"National Institutes of Health","grant_id":"5R56AG065352-02","title":"Combine Genomics and Symptoms Data Driven Models to Discover Synergistic Combinatory Therapies for Alzheimer's Disease"},{"funder_name":"National Institutes of Health","grant_id":"1R21AG078799-01A1","title":"AI models of multi-omic data integration for ming longevity core signaling pathways"}],"total_grants":4,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"green","license":"cc-by-nc-nd","oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2024/08/06/2024.08.01.606222.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2024.08.01.606222","host_type":"publisher"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11326168/pdf/nihpp-2024.08.01.606222v1.pdf","host_type":"repository"},{"url":"https://doi.org/10.1101/2024.08.01.606222","host_type":"Unpaywall"},{"url":"https://pubmed.ncbi.nlm.nih.gov/39149314","host_type":""},{"url":"http://dx.doi.org/10.1101/2024.08.01.606222","host_type":""}],"fields_of_study":["0206 medical engineering","02 engineering and technology"],"mesh_terms":[],"keywords":["Article"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-10T11:15:36.847449Z","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":[]}