CoMent

CoMent extracts and maps co-mentions of biomedical entities from scientific literature using formal identifiers and ontologies to generate interoperable relationship datasets.

Key Features:

• Text-mining co-mention detection: Detects significant co-mentions within scientific literature to identify relationships among diseases, symptoms, biological processes, molecular functions, and cellular compartments.
• Formal identifier mapping: Represents entities using formal identifiers from established biomedical resources, producing standardized, computer-tractable data for integration.
• Comprehensive relationships database: Aggregates millions of relationships among thousands of terms derived from co-mentions in scientific publications.

Scientific Applications:

• Disease–symptom associations: Retrieves comprehensive associations between complex diseases and reported symptoms to support curation and analysis.
• Biological process linkages: Links diseases to underlying biological processes to support investigation of disease mechanisms at the molecular level.
• Cross-disciplinary integration: Connects diverse biomedical concepts across resources to enable integrative, cross-disciplinary analyses.

Methodology:

Mines scientific literature to detect co-mentions of biomedical terms, infers relationships from those co-mentions, and maps entities to formal identifiers from major biomedical resources for standardization and interoperability.