BioREASONIC Dashboard
Analysis Overview
Key metrics and disease-gene relationships
Run New Analysis
Distribution Analysis
Gene Distribution by Disease
Disease Gene Overlap
Risk Assessment
Risk Score Distribution
Causal vs Risk Scores
Gene Classification
Shared Risk Genes
Shared Genes Analysis
These genes are associated with both Alzheimer Disease and Type 2 Diabetes, suggesting common biological pathways.
Alzheimer Disease Specific
Type 2 Diabetes Specific
Causal Risk Knowledge Graph (CRKG)
Interactive knowledge graph
CRKG Network Graph
Path Finder
Select source and target nodes to find causal paths between them in the knowledge graph.
KG Node Type Distribution
Distribution of entity types in the Causal Risk Knowledge Graph.
Edge Type Distribution
Causal Flow: Disease → Gene → Pathway
Node Connectivity Heatmap
Knowledge Graph Entities
| Entity | Type | Connections | Centrality | Disease Association |
|---|
CRKG Summary
The Causal Risk Knowledge Graph (CRKG) integrates multi-source biomedical data to identify causal relationships between diseases and risk genes.
Risk Gene Enrichment Analysis
GRASS-based risk prioritization
Risk Score Analysis
Top Risk Genes by Score
Risk Score vs Evidence
Risk Distribution
Risk Level Distribution
Risk Genes by Disease
Priority Candidates
Top Priority Risk Genes
Risk Gene Details
| Rank | Gene | Risk Score | Causal Score | Disease | Level |
|---|
ML-Based Gene Prioritization
Machine learning gene scoring
Priority Ranking
ML Gene Ranking
GRASS priority scores ranking genes by their causal evidence across multiple data sources.
Model Analysis
Feature Importance
Gene Clusters
Genes grouped by functional similarity and disease association patterns.
Score Components
Score Components Radar
Multi-dimensional view of GRASS scoring components per gene.
Score Distribution
Ranking Table
| # | Gene | Priority Score | Progress | Cluster | Type |
|---|
Pathway Enrichment Analysis
Biological pathway analysis
Top Enriched Pathways
Top Enriched Pathways
Combined pathway enrichment across multiple databases ranked by significance.
Gene Ontology
GO Biological Process
Top 10Enriched biological processes from Gene Ontology analysis.
GO Molecular Function
Top 10Molecular functions significantly associated with risk genes.
Pathway Databases
KEGG Pathways
Top 10KEGG pathway enrichment showing disease-related biological pathways.
Reactome Pathways
Top 10Reactome pathway analysis revealing molecular interaction networks.
Protein-Protein Interaction Network
Network Visualization
Interactive PPI Network
Protein-protein interactions from STRING database. Edge thickness indicates confidence score.
Network Metrics
Centrality Measures
Network centrality metrics identifying hub proteins with high connectivity.
Degree Distribution
Distribution of connection counts showing network topology characteristics.
Key Network Nodes
Hub Genes
Comprehensive Enrichment Analysis
Multi-database enrichment
Select Enrichment Databases
Disease & Association
Pathway & Ontology
Expression & Tissue
Interaction & Network
Drug & Pharmacogenomics
Cross-Database Comparison
Enrichment Summary
Top Enriched Terms
All CategoriesGWAS Catalog
DisGeNET
OpenTargets
ClinVar
KEGG Pathways
Reactome
Enrichr
WikiPathways
MSigDB
DrugBank
GTEx Tissue
Phenotypes
TISSUES Atlas
DISEASES
STRING PPI
IntAct
BioGRID
CORUM
Drug Target Analysis
Drug repurposing candidates
Drug Distribution
Drug Clinical Phases
Distribution of drugs targeting risk genes by clinical trial phase.
Drug Types
Classification of therapeutic agents by molecular type.
Drug Candidates
Drug Targets Table
| Drug | Type | Mechanism | Phase | Targets |
|---|
AI Assistant - BioREASONIC Analysis
LLM-powered analysis
Generating analysis summary from query results...
GRASS Analysis
Top Candidates
Enrichment
Validation
BioREASONIC Assistant
Ask me about the enrichment analysis results
Hello! I'm the BioREASONIC Explainer. I can analyze causal relationships between diseases using our knowledge graph with GRASS-based prioritization.
Ask me about genes, causal pathways, cross-disease mapping, or drug targets!
Contact & About
About BioREASONIC
BioREASONIC is a Causal-Oriented GraphRAG System for Multi-Aware Biomedical Reasoning. It integrates genetic fine-mapping, multi-omics annotations, and knowledge graph reasoning to identify causal genes and discover shared mechanisms across complex diseases.
The system features GRASS (Genetic Risk Aggregation Scoring System) for prioritizing causal genes from GWAS data, and constructs a Causal-Risk Knowledge Graph (CRKG) to enable interpretable biomedical reasoning and drug repurposing hypothesis generation.
Key Components
- GRASS: Genetic Risk Aggregation Scoring System
- CRKG: Causal-Risk Knowledge Graph
- BioREASONIC-Bench: Evaluation benchmark
- CARES: Causal Alignment Reasoning Evaluation Score
- BioREASONIC Explainer: LLM-powered reasoning
Institution & Contact
DeepCARES Lab
King Abdullah University of Science and Technology
Thuwal, Saudi Arabia
Research Team & Acknowledgments
Supported By
KAUST, Saudi Arabia
BioREASONIC is developed by the DeepCARES Lab at King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. The system integrates GraphRAG with causal reasoning for biomedical discovery.
Citation
title = {BioREASONIC: A Causal-Oriented GraphRAG System for Multi-Aware Biomedical Reasoning},
author = {Alsaedi, Sakhaa and Saif, Mohammed and Gojobori, Takashi and Gao, Xin},
email = {sakhaa.alsaedi@kaust.edu.sa, xin.gao@kaust.edu.sa},
journal = {bioRxiv},
year = {2025},
institution = {King Abdullah University of Science and Technology},
url = {https://deepcares.kaust.edu.sa/bioreasonic/}
}