18 research outputs found
Dynamic Discovery of Type Classes and Relations in Semantic Web Data
The continuing development of Semantic Web technologies and the increasing
user adoption in the recent years have accelerated the progress incorporating
explicit semantics with data on the Web. With the rapidly growing RDF (Resource
Description Framework) data on the Semantic Web, processing large semantic
graph data have become more challenging. Constructing a summary graph structure
from the raw RDF can help obtain semantic type relations and reduce the
computational complexity for graph processing purposes. In this paper, we
addressed the problem of graph summarization in RDF graphs, and we proposed an
approach for building summary graph structures automatically from RDF graph
data. Moreover, we introduced a measure to help discover optimum class
dissimilarity thresholds and an effective method to discover the type classes
automatically. In future work, we plan to investigate further improvement
options on the scalability of the proposed method
Beyond Known Reality: Exploiting Counterfactual Explanations for Medical Research
This study employs counterfactual explanations to explore "what if?"
scenarios in medical research, with the aim of expanding our understanding
beyond existing boundaries. Specifically, we focus on utilizing MRI features
for diagnosing pediatric posterior fossa brain tumors as a case study. The
field of artificial intelligence and explainability has witnessed a growing
number of studies and increasing scholarly interest. However, the lack of
human-friendly interpretations in explaining the outcomes of machine learning
algorithms has significantly hindered the acceptance of these methods by
clinicians in their clinical practice. To address this, our approach
incorporates counterfactual explanations, providing a novel way to examine
alternative decision-making scenarios. These explanations offer personalized
and context-specific insights, enabling the validation of predictions and
clarification of variations under diverse circumstances. Importantly, our
approach maintains both statistical and clinical fidelity, allowing for the
examination of distinct tumor features through alternative realities.
Additionally, we explore the potential use of counterfactuals for data
augmentation and evaluate their feasibility as an alternative approach in
medical research. The results demonstrate the promising potential of
counterfactual explanations to enhance trust and acceptance of AI-driven
methods in clinical settings
Evaluation of rigid-end offset effect on seismic behavior of a structure subjected to Van earthquake
Drug-drug interaction discovery and demystification using Semantic Web technologies
Objective: To develop a novel pharmacovigilance inferential framework to infer mechanistic explanations for asserted drug-drug interactions (DDIs) and deduce potential DDIs. Materials and Methods: A mechanism-based DDI knowledge base was constructed by integrating knowledge from several existing sources at the pharmacokinetic, pharmacodynamic, pharmacogenetic, and multipathway interaction levels. A query-based framework was then created to utilize this integrated knowledge base in conjunction with 9 inference rules to infer mechanistic explanations for asserted DDIs and deduce potential DDIs. Results: The drug-drug interactions discovery and demystification (D3) system achieved an overall 85% recall rate in terms of inferring mechanistic explanations for the DDIs integrated into its knowledge base, while demonstrating a 61% precision rate in terms of the inference or lack of inference of mechanistic explanations for a balanced, randomly selected collection of interacting and noninteracting drug pairs. Discussion: The successful demonstration of the D3 system's ability to confirm interactions involving well-studied drugs enhances confidence in its ability to deduce interactions involving less-studied drugs. In its demonstration, the D3 system infers putative explanations for most of its integrated DDIs. Further enhancements to this work in the future might include ranking interaction mechanisms based on likelihood of applicability, determining the likelihood of deduced DDIs, and making the framework publicly available. Conclusion: The D3 system provides an early-warning framework for augmenting knowledge of known DDIs and deducing unknown DDIs. It shows promise in suggesting interaction pathways of research and evaluation interest and aiding clinicians in evaluating and adjusting courses of drug therapy
F-18-FDG PET/CT imaging of metastatic testicular choriocarcinoma mimicking gastric cancer which initial symptom is melena
Gastric metastasis of choriocarcinoma is rarely reported in the literature. This case report presents the case of multiple metastatic testicular choriocarcinoma mimicking gastric cancer, with melena as the initial symptom. In this case, (18)fluorine-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) showed that the testis was the primary focus. The contribution of PET/CT is significant to primary focus detection in metastatic diseases of unknown primary origin that presented gastrointestinal bleeding. In addition to its use in staging of testicular carcinoma, PET/CT provides significant benefit in evaluating patients with increased levels of tumor markers and in detecting recurrence