HALD

HALD provides a knowledge graph consolidating entities and relations extracted from 339,918 PubMed‑indexed articles (September 2023) to support precision gerontology and geroscience analyses.

Key Features:

• Extensive Knowledge Integration: HALD integrates data across multiple modalities and contains 12,227 entities categorized into ten types: gene, RNA, protein, carbohydrate, lipid, peptide, pharmaceutical preparations, toxin, mutation, and disease.
• Rich Relationship Mapping: The knowledge graph encodes 115,522 relations interconnecting entities to represent interactions and associations relevant to aging and longevity.
• Biomarker Identification: HALD catalogs 1,855 aging biomarkers and 525 longevity biomarkers identified from the literature.
• Advanced Natural Language Processing (NLP): HALD applies text mining and advanced NLP algorithms for automated extraction of entities and relations from biomedical publications.
• Supports Multi‑faceted Analysis: The structured, interconnected dataset enables multi‑modal and multi‑faceted analyses for investigating mechanisms underlying aging and lifespan variation.

Scientific Applications:

• Mechanistic Insights: Exploration of biological pathways and entity relationships to identify potential molecular targets involved in aging processes.
• Precision Medicine Approaches: Mapping of biomarkers and relations to support personalized strategies tailored to individual genetic and molecular profiles.
• Cross‑disciplinary Research: Integration of diverse data types to facilitate interdisciplinary studies across genetics, biochemistry, pharmacology, and related fields in aging research.

Methodology:

HALD employs text mining of biomedical literature and leverages advanced NLP algorithms to extract and represent entities and relations from PubMed‑indexed articles.