In this work, we propose a method for the automated refinement of subject annotations in biomedical literature at the level of concepts. Semantic indexing and search of biomedical articles in MEDLINE/PubMed are based on semantic subject annotations with MeSH descriptors that may correspond to several related but distinct biomedical concepts. Such semantic annotations do not adhere to the level of detail available in the domain knowledge and may not be sufficient to fulfil the information needs of experts in the domain. To this end, we propose a new method that uses weak supervision to train a concept annotator on the literature available for a particular disease. We test this method on the MeSH descriptors for two diseases: Alzheimer's Disease and Duchenne Muscular Dystrophy. The results indicate that concept-occurrence is a strong heuristic for automated subject annotation refinement and its use as weak supervision can lead to improved concept-level annotations. The fine-grained semantic annotations can enable more precise literature retrieval, sustain the semantic integration of subject annotations with other domain resources and ease the maintenance of consistent subject annotations, as new more detailed entries are added in the MeSH thesaurus over time.
Overview of BioASQ 2020: The eighth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
National Center for Scientific Research “Demokritos”（“德谟克利特”国家科学研究中心）；Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）；National and Kapodistrian University of Athens（雅典国立卡波迪斯特里安大学）；Barcelona Supercomputing Center（巴塞罗那超级计算中心）
Overview of BioASQ 2022: The tenth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
National Center for Scientific Research “Demokritos”（希腊德谟克利特国家科学研究中心）；Aristotle University of Thessaloniki（亚里士多德大学塞萨洛尼基分校）；Barcelona Supercomputing Center（巴塞罗那超级计算中心）
Overview of BioASQ 2026: The fourteenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
National Center for Scientific Research “Demokritos”（德谟克利特国家科学研究中心）；Barcelona Supercomputing Center（巴塞罗那超级计算中心）；Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）；Artificial Intelligence Research Institute（人工智能研究院）；HSE University（高等经济大学）；Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
Overview of BioASQ 2025: The Thirteenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
Barcelona Supercomputing Center, Barcelona, Spain（巴塞罗那超级计算中心，巴塞罗那，西班牙）；Moscow State University, Russia（莫斯科国立大学，俄罗斯）；Artificial Intelligence Research Institute, Russia（人工智能研究 institute，俄罗斯）；Kazan Federal University, Russia（卡扎诺夫联邦大学，俄罗斯）；SberAI & Skoltech, Russia（SberAI 与 Skoltech，俄罗斯）；Aristotle University of Thessaloniki, Greece（亚里士多德大学，希腊）；Northwell Health, USA（Northwell Health，美国）；University of Padua, Italy（帕多瓦大学，意大利）；Archimedes, Athena Research Center, Greece（阿基米德，亚特兰蒂斯研究中心，希腊）
The MeSH-gram Neural Network Model: Extending Word Embedding Vectors with MeSH Concepts for UMLS Semantic Similarity and Relatedness in the Biomedical Domain
