The process of refining the research question in a medical study depends greatly on the current background of the investigated subject. The information found in prior works can directly impact several stages of the study, namely the cohort definition stage. Besides previous published methods, researchers could also leverage on other materials, such as the output of cohort selection tools, to enrich and to accelerate their own work. However, this kind of information is not always captured by search engines. In this paper, we present a methodology, based on a combination of content-based retrieval and text annotation techniques, to identify relevant scientific publications related to a research question and to the selected data sources.
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http://dx.doi.org/10.3233/SHTI210174 | DOI Listing |
BMC Bioinformatics
January 2025
Institute of Computer Science, University of Rostock, 18051, Rostock, Germany.
Background: Interpretability is a topical question in recommender systems, especially in healthcare applications. An interpretable classifier quantifies the importance of each input feature for the predicted item-user association in a non-ambiguous fashion.
Results: We introduce the novel Joint Embedding Learning-classifier for improved Interpretability (JELI).
Neural Netw
January 2025
LISAC Laboratory, Department of Informatics, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, 1796 Fez-Atlas, Fez, 30000, Morocco. Electronic address:
Session-based recommendation systems (SBRS) are essential for enhancing the customer experience, improving sales and loyalty, and providing the possibility to discover products in dynamic and real-world scenarios without needing user history. Despite their importance, traditional or even current SBRS algorithms face limitations, notably the inability to capture complex item transitions within each session and the disregard for general patterns that can be derived from multiple sessions. This paper proposes a novel SBRS model, called Capsule GraphSAGE for Session-Based Recommendation (CapsGSR), that marries GraphSAGE's scalability and inductive learning capabilities with the Capsules network's abstraction levels by generating multiple integrations for each node from different perspectives.
View Article and Find Full Text PDFJ Am Chem Soc
January 2025
Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
This research focused on the efficient collection of experimental metal-organic framework (MOF) data from scientific literature to address the challenges of accessing hard-to-find data and improving the quality of information available for machine learning studies in materials science. Utilizing a chain of advanced large language models (LLMs), we developed a systematic approach to extract and organize MOF data into a structured format. Our methodology successfully compiled information from more than 40,000 research articles, creating a comprehensive and ready-to-use data set.
View Article and Find Full Text PDFF1000Res
January 2025
Prince of Songkla University, Hat Yai, Songkhla, 90110, Thailand.
Background: The demand for online education promotion platforms has increased. In addition, the digital library system is one of the many systems that support teaching and learning. However, most digital library systems store books in the form of libraries that were developed or purchased exclusively by the library, without connecting data with different agencies in the same system.
View Article and Find Full Text PDFSci Rep
January 2025
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong, China.
Knowledge-aware recommendation systems often face challenges owing to sparse supervision signals and redundant entity relations, which can diminish the advantages of utilizing knowledge graphs for enhancing recommendation performance. To tackle these challenges, we propose a novel recommendation model named Dual-Intent-View Contrastive Learning network (DIVCL), inspired by recent advancements in contrastive and intent learning. DIVCL employs a dual-view representation learning approach using Graph Neural Networks (GNNs), consisting of two distinct views: a local view based on the user-item interaction graph and a global view based on the user-item-entity knowledge graph.
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