GeneDive

GeneDive aggregates gene–drug–disease (DGR) interactions extracted from biomedical literature to support pharmacogenomics and precision medicine research.

Key Features:

• Data sources: Combines interaction data derived from manual curation and automated data mining of biomedical literature.
• Data management: Associates interactions with confidence scores, source articles, and contextual excerpts to distinguish high-quality from mixed-quality evidence.
• Search, sorting, grouping, filtering, highlighting, and visualization: Enables searching, sorting, grouping, filtering, highlighting, and visualization of interaction records to identify explicit and emergent relationships.
• Supporting evidence accessibility: Exposes supporting evidence and permits adjustment of data quality thresholds to control inclusion of interactions based on confidence and evidence.
• Integration of user data: Supports import of user-provided interaction datasets without necessitating data sharing to enable private hypothesis testing.
• Exploratory research support: Provides surrounding literature context for interactions to support discovery of relationships not explicitly stated in single articles.
• Precision medicine applicability: Facilitates application of literature-derived gene–drug–disease interactions in pharmacogenomics and clinical scenario analyses.

Scientific Applications:

• Pharmacogenomics research: Enables discovery and evaluation of gene–drug interactions relevant to pharmacogenomic studies.
• Precision medicine analyses: Supports translation of literature-derived interactions into patient-specific clinical and therapeutic considerations.
• Hypothesis generation and testing: Enables generation and testing of hypotheses by integrating literature-derived interactions with user datasets.
• Longitudinal and exploratory investigations: Supports longitudinal assessment of evidence quality and exploratory analysis of interaction context across the literature.

Methodology:

Interaction data are obtained from manual curation and automated data mining of biomedical literature and are associated with confidence scores, source-article excerpts, and are made searchable, sortable, groupable, filterable, highlightable, visualizable, and importable with user-provided datasets.