Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Schemas01:42

Schemas

A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
Self-Schemas02:16

Self-Schemas

In general, a schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
ER Retrieval Pathway01:45

ER Retrieval Pathway

In the secretory pathway, vesicles transport proteins from one cellular compartment to another in forward transport to deliver the protein to its correct location. Occasionally, misfolded proteins and incorrect proteins escape their original compartments, and a retrieval pathway is used to return the escaped proteins to their original compartment.
The ER uses many checkpoints to prevent the entry of incorrectly folded or a resident protein as cargo onto a transport vesicle. These mechanisms...
Impact of Schemas01:30

Impact of Schemas

Schemas are cognitive structures that provide a framework for interpreting and organizing social information. They help individuals navigate complex environments by offering expectations about people, events, and behaviors. Schemas influence attention, encoding, and retrieval processes, thereby shaping the entire trajectory of information processing in social contexts.Attention and Cognitive LoadDuring initial attention, schemas function as filters that prioritize schema-consistent information,...
Schemata01:17

Schemata

A schema is a mental construct that organizes related concepts, allowing the brain to process information efficiently. Upon activation, schemata facilitate assumptions about people or objects.
Two types of schemata are:
Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Early antifungal resistance prediction based on MALDI-TOF mass spectrometry and machine learning.

Scientific reports·2026
Same author

Translational bioinformatics stalls at implementation.

Briefings in bioinformatics·2026
Same author

Clustering of disease trajectories with explainable machine learning: A case study on postoperative delirium phenotypes.

PLOS digital health·2026
Same author

Peripheral Oxygen Saturation Targets and Hyperoxemia in Critical Care: Influence of pH, F<sub>i</sub>O<sub>2</sub>, and Respiratory Failure.

Antioxidants (Basel, Switzerland)·2026
Same author

Uncovering Cas9 PAM diversity through metagenomic mining and machine learning.

Nature communications·2026
Same author

Uncertainty modeling in multimodal speech analysis across the psychosis spectrum.

NPJ digital medicine·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Videos

Semantic web data warehousing for caGrid.

Jamie P McCusker1, Joshua A Phillips, Alejandra González Beltrán

  • 1Department of Pathology, Yale University School of Medicine, New Haven, CT, USA

BMC Bioinformatics
|October 3, 2009
PubMed
Summary
This summary is machine-generated.

Corvus, a Semantic Web data warehouse, integrates cancer data from caGrid by transforming UML models into OWL ontologies. This approach enables semantic integration of distributed web services for researchers.

Related Experiment Videos

Area of Science:

  • Bioinformatics
  • Data Science
  • Semantic Web Technologies

Background:

  • The National Cancer Institute's caGrid facilitates sharing cancer data and services.
  • Integrating diverse datasets on caGrid requires effective methods for accessing and combining information.
  • Current caGrid clients must infer relationships between data models, posing integration challenges.

Purpose of the Study:

  • To present Corvus, a Semantic Web-based data warehouse designed to establish relationships among caGrid data models.
  • To demonstrate a method for transforming semantically-annotated caBIG Unified Modeling Language (UML) information models into Web Ontology Language (OWL) ontologies.
  • To address the need for semantic integration in distributed web service environments.

Main Methods:

  • Developed Corvus, a Semantic Web data warehouse.
  • Transformed caBIG UML information models into OWL ontologies, preserving semantic annotations.
  • Utilized Semantic Extraction, Transformation, and Loading (SETL) for data integration.
  • Aligned and queried data from caTissue and caArray caGrid data sources within Corvus.

Main Results:

  • Successfully transformed UML models into OWL ontologies, enabling semantic interoperability.
  • Demonstrated the capability to integrate and query data from disparate caGrid sources (caTissue, caArray) via Corvus.
  • Validated the effectiveness of Corvus in creating relationships among different caGrid data models.

Conclusions:

  • Semantic integration is crucial for effectively integrating data from distributed web services.
  • Corvus provides a valuable solution for achieving semantic integration of cancer data within the caGrid ecosystem.
  • The presented approach is generalizable and beneficial for researchers facing similar data integration challenges.