BECAS

BECAS annotates biomedical texts to identify and link biomedical concepts in MEDLINE/PubMed abstracts and free text for biomedical literature curation and analysis.

Key Features:

• Multi-Concept Type Annotation: Supports selection and annotation of multiple concept types within the same text.
• Reference Database Linking: Enriches identified concepts with links to external reference databases for further information.
• Nested and Intercepted Concept Annotation: Automatically detects and annotates nested and intercepted concepts within entity spans.
• REST API Access: Exposes all text-processing and annotation functionalities via an HTTP REST API.
• Input Support: Processes MEDLINE/PubMed abstracts and arbitrary free text as input sources.
• Concept Coverage: Identifies and annotates over 1,200,000 biomedical concepts.
• Customizable Parameters: Provides configurable parameters for concept-type selection and reference-database referencing.

Scientific Applications:

• Literature curation and systematic reviews: Annotation of MEDLINE/PubMed abstracts to support systematic reviews and meta-analyses.
• Bioinformatics and computational biology: Extraction of biomedical concepts for integration into computational analyses and text-mining studies.
• Medical informatics and clinical decision support: Concept identification and linking to support clinical decision support systems and informatics workflows.
• Knowledgebase enrichment: Linking annotations to external databases to augment biomedical knowledge graphs and resources.

Methodology:

BeCAS employs advanced text-mining techniques to process and annotate biomedical texts, uses customizable parameters for concept-type selection and reference-database linking, and provides access to its text-processing and annotation functions via an HTTP REST API.