HFIP

HFIP integrates multi-omics datasets and curated knowledge to enable data-driven discovery and precision medicine in heart failure.

Key Features:

• Multi-omics data integration: Aggregates and integrates multi-omics datasets to consolidate fragmented HF-related data.
• Dataset collection: Includes 253 datasets encompassing 7,842 samples sourced from public repositories.
• Analytical toolset: Provides 14 independent analytical tools to support diverse types of analyses.
• Data fusion and visualization: Enables fusion analysis and visualization of integrated multi-omics data.
• Auto-updating mechanism: Continuously integrates new data and knowledge as they become available.
• HF knowledge base: Maintains a curated knowledge base containing 1,956 genes with detailed annotations.
• Scoring system: Implements a scoring system to evaluate relationships between molecular signals and heart failure.
• Literature mining: Provides a literature mining module that summarizes research hotspots and contexts in basic and clinical HF research.

Scientific Applications:

• Precision medicine in heart failure: Supports discovery and stratification efforts to advance precision medicine approaches for HF.
• Biomarker and target identification: Aids identification of potential biomarkers and therapeutic targets through integrated multi-omics analysis and scoring.
• Multi-omics exploration: Facilitates comprehensive exploration and fusion analysis of genomics, transcriptomics, and other omics data related to HF.
• Research trend analysis: Enables overview of basic and clinical research hotspots via literature mining and knowledge integration.

Methodology:

Aggregates and integrates 253 multi-omics datasets (7,842 samples) from public repositories; deploys 14 analytical tools; performs data fusion analysis and visualization; employs an auto-updating data/knowledge ingestion mechanism; constructs a 1,956-gene annotated knowledge base; applies a scoring system to relate molecular signals to heart failure; and conducts literature mining to extract research hotspots and contexts.