14 papers · ranked by Valyu relevance
Zixuan Zhu, Yuhai Zhao
Recently, Multi-Graph Learning was proposed as the extension of Multi-Instance Learning and has achieved some successes. However, to the best of our knowledge, currently, there is no study working on Multi-Graph Multi-Label Learning, where each object is represented as a bag containing a number of graphs and each bag…
Lars Vogt, Tobias Kuhn, Robert Hoehndorf
Background In today’s landscape of data management, the importance of knowledge graphs and ontologies is escalating as critical mechanisms aligned with the FAIR Guiding Principles-ensuring data and metadata are Findable, Accessible, Interoperable, and Reusable. We discuss three challenges that may hinder the effective…
Gabriel Budel, Ying Jin, Piet Van Mieghem, Maksim Kitsak
Interpreting natural language is an increasingly important task in computer algorithms due to the growing availability of unstructured textual data. Natural Language Processing (NLP) applications rely on semantic networks for structured knowledge representation. The fundamental properties of semantic networks must be…
Asmaa M. El-Said, Ali I. Eldesoky, Hesham A. Arafat
Tremendous growth in the number of textual documents has produced daily requirements for effective development to explore, analyze, and discover knowledge from these textual documents. Conventional text mining and managing systems mainly use the presence or absence of key words to discover and analyze useful…
Ming He, Chen Huang, Bo Liu, Yadong Wang + 1 more
Background Exploring the relationship between disease and gene is of great significance for understanding the pathogenesis of disease and developing corresponding therapeutic measures. The prediction of disease-gene association by computational methods accelerates the process. Results Many existing methods cannot fully…
Saeed Salem, Cagri Ozcaglar
Background Advances in genomic technologies have enabled the accumulation of vast amount of genomic data, including gene expression data for multiple species under various biological and environmental conditions. Integration of these gene expression datasets is a promising strategy to alleviate the challenges of…
João D. Ferreira, Francisco M. Couto
Background Given the increasing amount of biomedical resources that are being annotated with concepts from more than one ontology and covering multiple domains of knowledge, it is important to devise mechanisms to compare these resources that take into account the various domains of annotation. For example, metabolic…
Gunther Eysenbach, Illhoi Yoo, Sarah Lim Choi Keung, Ali Zahrawi + 8 more
Background The Unified Medical Language System (UMLS) contains many important ontologies in which terms are connected by semantic relations. For many studies on the relationships between biomedical concepts, the use of transitively associated information from ontologies and the UMLS has been shown to be effective.…
Ciyuan Peng, Feng Xia, Mehdi Naseriparsa, Francesco Osborne
With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As graph data, knowledge graphs accumulate and convey knowledge of the real world. It has been well-recognized that knowledge graphs…
Anna Kirkpatrick, Chidozie Onyeze, David Kartchner, Stephen Allegri + 4 more
Information Networks: SemNet 2.0 Authors: ['Anna Kirkpatrick' 'Chidozie Onyeze' 'David Kartchner' 'Stephen Allegri' 'Davi Nakajima An' 'Kevin McCoy' 'Evie Davalbhakta' 'Cassie S. Mitchell'] Literature-based discovery (LBD) summarizes information and generates insight from large text corpuses. The SemNet framework…
Eric Ke Wang, Futai Zou
With the development of social networks, people have started to use social network tools to record their life and work more and more frequently. How to analyze social networks to explore potential characteristics and trend of social events has been a hot research topic. In order to analyze it effectively, a kind of…
Dirk Weissenborn, Michael Schroeder, George Tsatsaronis
Background The complexity and scale of the knowledge in the biomedical domain has motivated research work towards mining heterogeneous data from both structured and unstructured knowledge bases. Towards this direction, it is necessary to combine facts in order to formulate hypotheses or draw conclusions about the…
Andre Lamurias, Pedro Ruas, Francisco M. Couto
Background Biomedical literature concerns a wide range of concepts, requiring controlled vocabularies to maintain a consistent terminology across different research groups. However, as new concepts are introduced, biomedical literature is prone to ambiguity, specifically in fields that are advancing more rapidly, for…
Marcel H Schulz, Sebastian Köhler, Sebastian Bauer, Peter N Robinson
Background Semantic similarity searches in ontologies are an important component of many bioinformatic algorithms, e.g., finding functionally related proteins with the Gene Ontology or phenotypically similar diseases with the Human Phenotype Ontology (HPO). We have recently shown that the performance of semantic…