Journal of Biomedical Semantics最新文献

Background: Melanoma is one of the least common but the deadliest of skin cancers. This cancer begins when the genes of a cell suffer damage or fail, and identifying the genes involved in melanoma is crucial for understanding the melanoma tumorigenesis. Thousands of publications about human melanoma appear every year. However, while biological curation of data is costly and time-consuming, to date the application of machine learning for gene-melanoma relation extraction from text has been severely limited by the lack of annotated resources.

Results: To overcome this lack of resources for melanoma, we have exploited the information of the Melanoma Gene Database (MGDB, a manually curated database of genes involved in human melanoma) to automatically build an annotated dataset of binary relations between gene and melanoma entities occurring in PubMed abstracts. The entities were automatically annotated by state-of-the-art text-mining tools. Their annotation includes both the mention text spans and normalized concept identifiers. The relations among the entities were annotated at concept- and mention-level. The concept-level annotation was produced using the information of the genes in MGDB to decide if a relation holds between a gene and melanoma concept in the whole abstract. The exploitability of this dataset was tested with both traditional machine learning, and neural network-based models like BERT. The models were then used to automatically extract gene-melanoma relations from the biomedical literature. Most of the current models use context-aware representations of the target entities to establish relations between them. To facilitate researchers in their experiments we generated a mention-level annotation in support to the concept-level annotation. The mention-level annotation was generated by automatically linking gene and melanoma mentions co-occurring within the sentences that in MGDB establish the association of the gene with melanoma.

Conclusions: This paper presents a corpus containing gene-melanoma annotated relations. Additionally, it discusses experiments which show the usefulness of such a corpus for training a system capable of mining gene-melanoma relationships from the literature. Researchers can use the corpus to develop and compare their own models, and produce results which might be integrated with existing structured knowledge databases, which in turn might facilitate medical research.

Background: The advancement of science and technologies play an immense role in the way scientific experiments are being conducted. Understanding how experiments are performed and how results are derived has become significantly more complex with the recent explosive growth of heterogeneous research data and methods. Therefore, it is important that the provenance of results is tracked, described, and managed throughout the research lifecycle starting from the beginning of an experiment to its end to ensure reproducibility of results described in publications. However, there is a lack of interoperable representation of end-to-end provenance of scientific experiments that interlinks data, processing steps, and results from an experiment's computational and non-computational processes.

Results: We present the "REPRODUCE-ME" data model and ontology to describe the end-to-end provenance of scientific experiments by extending existing standards in the semantic web. The ontology brings together different aspects of the provenance of scientific studies by interlinking non-computational data and steps with computational data and steps to achieve understandability and reproducibility. We explain the important classes and properties of the ontology and how they are mapped to existing ontologies like PROV-O and P-Plan. The ontology is evaluated by answering competency questions over the knowledge base of scientific experiments consisting of computational and non-computational data and steps.

Conclusion: We have designed and developed an interoperable way to represent the complete path of a scientific experiment consisting of computational and non-computational steps. We have applied and evaluated our approach to a set of scientific experiments in different subject domains like computational science, biological imaging, and microscopy.

Background: The activation degree of the orbitofrontal cortex (OFC) functional area in drug abusers is directly related to the craving for drugs and the tolerance to punishment. Currently, among the clinical research on drug rehabilitation, there has been little analysis of the OFC activation in individuals abusing different types of drugs, including heroin, methamphetamine, and mixed drugs. Therefore, it becomes urgently necessary to clinically investigate the abuse of different drugs, so as to explore the effects of different types of drugs on the human brain.

Methods: Based on prefrontal high-density functional near-infrared spectroscopy (fNIRS), this research designs an experiment that includes resting and drug addiction induction. Hemoglobin concentrations of 30 drug users (10 on methamphetamine, 10 on heroin, and 10 on mixed drugs) were collected using fNIRS and analyzed by combining algorithm and statistics.

Results: Linear discriminant analysis (LDA), Support vector machine (SVM) and Machine-learning algorithm was implemented to classify different drug abusers. Oxygenated hemoglobin (HbO2) activations in the OFC of different drug abusers were statistically analyzed, and the differences were confirmed. Innovative findings: in both the Right-OFC and Left-OFC areas, methamphetamine abusers had the highest degree of OFC activation, followed by those abusing mixed drugs, and heroin abusers had the lowest. The same result was obtained when OFC activation was investigated without distinguishing the left and right hemispheres.

Conclusions: The findings confirmed the significant differences among different drug abusers and the patterns of OFC activations, providing a theoretical basis for personalized clinical treatment of drug rehabilitation in the future.

Background: Medical experts in the domain of Diabetes Mellitus (DM) acquire specific knowledge from diabetic patients through monitoring and interaction. This allows them to know the disease and information about other conditions or comorbidities, treatments, and typical consequences of the Mexican population. This indicates that an expert in a domain knows technical information about the domain and contextual factors that interact with it in the real world, contributing to new knowledge generation. For capturing and managing information about the DM, it is necessary to design and implement techniques and methods that allow: determining the most relevant conceptual dimensions and their correct organization, the integration of existing medical and clinical information from different resources, and the generation of structures that represent the deduction process of the doctor. An Ontology Network is a collection of ontologies of diverse knowledge domains which can be interconnected by meta-relations. This article describes an Ontology Network for representing DM in Mexico, designed by a proposed methodology. The information used for Ontology Network building include the ontological resource reuse and non-ontological resource transformation for ontology design and ontology extending by natural language processing techniques. These are medical information extracted from vocabularies, taxonomies, medical dictionaries, ontologies, among others. Additionally, a set of semantic rules has been defined within the Ontology Network to derive new knowledge.

Results: An Ontology Network for DM in Mexico has been built from six well-defined domains, resulting in new classes, using ontological and non-ontological resources to offer a semantic structure for assisting in the medical diagnosis process. The network comprises 1367 classes, 20 object properties, 63 data properties, and 4268 individuals from seven different ontologies. Ontology Network evaluation was carried out by verifying the purpose for its design and some quality criteria.

Conclusions: The composition of the Ontology Network offers a set of well-defined ontological modules facilitating the reuse of one or more of them. The inclusion of international vocabularies as SNOMED CT or ICD-10 reinforces the representation by international standards. It increases the semantic interoperability of the network, providing the opportunity to integrate other ontologies with the same vocabularies. The ontology network design methodology offers a guide for ontology developers about how to use ontological and non-ontological resources in order to exploit the maximum of information and knowledge from a set of domains that share or not information.