{"doi":"10.1109/access.2025.3648945","title":"Dual-Branch Semi-Supervised Deep Learning for Improved Lung Cancer Diagnosis With Metabolomics","abstract":null,"journal":"IEEE Access","year":2026,"id":638095,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"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":1657126,"name":"Xinbo Liu","orcid":null,"position":1,"is_corresponding":false},{"id":420516,"name":"Chao Ye","orcid":"0000-0002-9897-0085","position":2,"is_corresponding":false},{"id":1657127,"name":"Jinze Zhang","orcid":null,"position":3,"is_corresponding":false},{"id":1657128,"name":"Xuanyu Meng","orcid":null,"position":4,"is_corresponding":false},{"id":951270,"name":"Jin Guo","orcid":"0000-0002-0093-1927","position":5,"is_corresponding":false},{"id":1657129,"name":"Xianjun Min","orcid":null,"position":6,"is_corresponding":false},{"id":1094466,"name":"Qian Li","orcid":"0009-0002-0336-4021","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Dual-Branch Semi-Supervised Deep Learning for Improved Lung Cancer Diagnosis With Metabolomics","abstract":"Metabolomics, a comprehensive omics approach for profiling small-molecule metabolites within biological systems, has become an indispensable tool for disease diagnosis, prognostic assessment, and prediction of therapeutic response. In lung cancer research, metabolomics shows particular promise, notably in early detection, subtype classification, treatment monitoring, and outcome prediction. Lung cancer remains one of the leading causes of cancer-related mortality worldwide, characterized by high treatment costs and persistently low 5-year survival rates, underscoring the urgent need for improved diagnostic strategies. The Lung Cancer Metabolome Database (LCMD) is a pioneering, freely accessible online resource designed to address these challenges by providing a comprehensive repository of lung cancer-related metabolites identified from mass spectrometry-based studies. Lung cancer remains a leading global cause of cancer related mortality, with a critical need for early diagnostic tools. We propose a novel Dual Branch Semi-supervised deep Learning (DBSL) framework to address metabolomics data challenges limited labeled samples and high missingness using the Lung Cancer Metabolome Database (LCMD). We design a global temporal patterns and local feature interactions (a novel Multi-Features Consistency Attention, MFCA).We design a semi-supervised optimization for leveraging pseudo labeling, stochastic augmentation, and consistency loss to exploit unlabeled data. Evaluated under extreme low label regimes (5%, 10%, 15% labeled data), DBSL achieves state of the art performance on the LCMD dataset. These results demonstrate DBSL’s potential for scalable, cost effective lung cancer diagnostics, directly addressing the urgent clinical need for early intervention in Lung Cancer.","is_dataset_classified":null,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W7117503711","authors":[],"funders":[{"funder_name":"Beijing Science and Technology Innovation Medical Development Fund","grant_id":"KC2023-JX-0186-PM021","title":null},{"funder_name":"Beijing Science and Technology Innovation Medical Development Fund","grant_id":"KC2023-JX-0186-PQ013","title":null},{"funder_name":"Hebei Province Overseas Students Introduction Funding Project","grant_id":"C20220510","title":null}],"total_grants":3,"fwci":0.4868,"citation_percentile":0.71118382,"influential_citations":0,"citation_trend":[{"year":2026,"count":1}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://doi.org/10.1109/access.2025.3648945","host_type":"journal"},{"url":"https://doi.org/10.1109/access.2025.3648945","host_type":"publisher"},{"url":"http://xplorestaging.ieee.org/ielx8/6287639/11323511/11316468.pdf?arnumber=11316468","host_type":"publisher"}],"fields_of_study":["Metabolomics and Mass Spectrometry Studies","Machine Learning in Bioinformatics","Ferroptosis and cancer prognosis"],"mesh_terms":[],"keywords":["Metabolome","Lung cancer","Metabolomics","Deep learning","Biomarker discovery","Cancer","Disease","Profiling (computer programming)"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T20:05:58.984379Z","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":[]}