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There are a huge variety of data sources relevant to chemical, biological and pharmacological research, but these data sources are highly siloed and cannot be queried together in a straightforward way. Semanti...
Automatic extracting protein entity interaction information from biomedical literature can help to build protein relation network and design new drugs. There are more than 20 million literature abstracts inclu...
This article is part of a Supplement: Volume 8 Supplement 1
Although disease diagnosis has greatly benefited from next generation sequencing technologies, it is still difficult to make the right diagnosis purely based on sequencing technologies for many diseases with c...
This article is part of a Supplement: Volume 8 Supplement 1
Discovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes...
This article is part of a Supplement: Volume 8 Supplement 1
Biological environment is uncertain and its dynamic is similar to the multiagent environment, thus the research results of the multiagent system area can provide valuable insights to the understanding of biolo...
This article is part of a Supplement: Volume 8 Supplement 1
In recent years, numerous computational methods predicted protein function based on the protein-protein interaction (PPI) network. These methods supposed that two proteins share the same function if they inter...
This article is part of a Supplement: Volume 8 Supplement 1
Observational clinical studies play a pivotal role in advancing medical knowledge and patient healthcare. To lessen the prohibitive costs of conducting these studies and support evidence-based medicine, result...
Next Generation Sequencing (NGS) is playing a key role in therapeutic decision making for the cancer prognosis and treatment. The NGS technologies are producing a massive amount of sequencing datasets. Often, ...
Bio-ontologies are becoming increasingly important in knowledge representation and in the machine learning (ML) fields. This paper presents a ML approach that incorporates bio-ontologies and its application to...
Knowledge representation frameworks are essential to the understanding of complex biomedical processes, and to the analysis of biomedical texts that describe them. Combined with natural language processing (NL...
Medical personnel in hospitals often works under great physical and mental strain. In medical decision-making, errors can never be completely ruled out. Several studies have shown that between 50 and 60% of ad...
Biological sciences are characterised not only by an increasing amount but also the extreme complexity of its data. This stresses the need for efficient ways of integrating these data in a coherent description...
Patient data, such as electronic health records or adverse event reporting systems, constitute an essential resource for studying Adverse Drug Events (ADEs). We explore an original approach to identify frequen...
Structured data acquisition is a common task that is widely performed in biomedicine. However, current solutions for this task are far from providing a means to structure data in such a way that it can be auto...
Disease taxonomies have been designed for many applications, but they tend not to fully incorporate the growing amount of molecular-level knowledge of disease processes, inhibiting research efforts. Understand...
Biological databases store data about laboratory experiments, together with semantic annotations, in order to support data aggregation and retrieval. The exact meaning of such annotations in the context of a d...
High-throughput methods in molecular biology provided researchers with abundance of experimental data that need to be interpreted in order to understand the experimental results. Manual methods of functional g...
Medical ontologies are expected to contribute to the effective use of medical information resources that store considerable amount of data. In this study, we focused on disease ontology because the complicated...
Ontologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data integration and in...
We present the Europe PMC literature component of Open Targets - a target validation platform that integrates various evidence to aid drug target identification and validation. The component identifies target-...
Detailed Clinical Models (DCMs) have been regarded as the basis for retaining computable meaning when data are exchanged between heterogeneous computer systems. To better support clinical cancer data capturing...
Bio-ontologies typically require multiple axes of classification to support the needs of their users. Development of such ontologies can only be made scalable and sustainable by the use of inference to automat...
Research for ontology evaluation is scarce. If biomedical ontological datasets and knowledgebases are to be widely used, there needs to be quality control and evaluation for the content and structure of the on...
A standard task in pharmacogenomics research is identifying genes that may be involved in drug response variability, i.e., pharmacogenes. Because genomic experiments tended to generate many false positives, co...
MeSH indexing is the task of assigning relevant MeSH terms based on a manual reading of scholarly publications by human indexers. The task is highly important for improving literature retrieval and many other ...
Single Nucleotide Polymorphisms (SNPs) are among the most important types of genetic variations influencing common diseases and phenotypes. Recently, some corpora and methods have been developed with the purpo...
Biomedical data, e.g. from knowledge bases and ontologies, is increasingly made available following open linked data principles, at best as RDF triple data. This is a necessary step towards unified access to b...
Pathogenic Escherichia coli infections cause various diseases in humans and many animal species. However, with extensive E. coli vaccine research, we are still unable to fully protect ourselves against E. coli in...
Integrating multiple sources of pharmacovigilance evidence has the potential to advance the science of safety signal detection and evaluation. In this regard, there is a need for more research on how to integr...
The Drug Ontology (DrOn) is an OWL2-based representation of drug products and their ingredients, mechanisms of action, strengths, and dose forms. We originally created DrOn for use cases in comparative effecti...
Analysing public opinions on HPV vaccines on social media using machine learning based approaches will help us understand the reasons behind the low vaccine coverage and come up with corresponding strategies t...
Facing a growing workload and dwindling resources, the US National Library of Medicine (NLM) created the Indexing Initiative project in 1996. This cross-library team’s mission is to explore indexing methodolog...
Ontologies are widely used as metadata in biological and biomedical datasets. Measures of semantic similarity utilize ontologies to determine how similar two entities annotated with classes from ontologies are...
Identifying incident cancer cases within a population remains essential for scientific research in oncology. Data produced within electronic health records can be useful for this purpose. Due to the multiplici...
Several query federation engines have been proposed for accessing public Linked Open Data sources. However, in many domains, resources are sensitive and access to these resources is tightly controlled by stake...
Ectopic pregnancy is a frequent early complication of pregnancy associated with significant rates of morbidly and mortality. The positive diagnosis of this condition is established through transvaginal ultraso...
Drug product data is available on the Web in a distributed fashion. The reasons lie within the regulatory domains, which exist on a national level. As a consequence, the drug data available on the Web are inde...
Drug ontologies could help pharmaceutical researchers overcome information overload and speed the pace of drug discovery, thus benefiting the industry and patients alike. Drug-disease relations, specifically d...
Biomedical ontologies aim at providing the most exhaustive and rigorous representation of reality as described by biomedical sciences. A large part of medical reasoning deals with diagnosis and is essentially ...
Semantic relatedness is a measure that quantifies the strength of a semantic link between two concepts. Often, it can be efficiently approximated with methods that operate on words, which represent these conce...
This special issue covers selected papers from the 18th Bio-Ontologies Special Interest Group meeting and Phenotype Day, which took place at the Intelligent Systems for Molecular Biology (ISMB) conference in D...
The systematic analysis of a large number of comparable plant trait data can support investigations into phylogenetics and ecological adaptation, with broad applications in evolutionary biology, agriculture, c...
Objectives of this work are to (1) present an ontological framework for the TNM classification system, (2) exemplify this framework by an ontology for colon and rectum tumours, and (3) evaluate this ontology b...
The biomedical community has now developed a significant number of ontologies. The curation of biomedical ontologies is a complex task and biomedical ontologies evolve rapidly, so new versions are regularly an...
Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) has been designed as standard clinical terminology for annotating Electronic Health Records (EHRs). EHRs textual information is used to classi...
Twitter updates now represent an enormous stream of information originating from a wide variety of formal and informal sources, much of which is relevant to real-world events. They can therefore be highly usef...
One of the current research efforts in the area of biomedicine is the representation of knowledge in a structured way so that reasoning can be performed on it. More precisely, in the field of physiotherapy, in...
The process of discovering new drugs is a lengthy, time-consuming and expensive process. Modern day drug discovery relies heavily on the rapid identification of novel ‘targets’, usually proteins that can be mo...
Research on medical vocabulary expansion from large corpora has primarily been conducted using text written in English or similar languages, due to a limited availability of large biomedical corpora in most la...
The Environment Ontology (ENVO; http://www.environmentontology.org/), first described in 2013, is a resource and research target for the semantically c...
2022 Citation Impact
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Journal of Biomedical Semantics