Integrated Data System
NGLIS is a platform that supports end-to-end genomic testing workflows,
from hospital orders to data analysis and reporting.
NGLIS
(Next-Generation Genomic
Laboratory Information System)
NGLIS is an integrated platform that connects hospital EMR systems with
genomic analysis software to manage and standardize the entire genomic
testing workflow, from test orders to data analysis and reporting.
With SOP-based workflows, quality control mechanisms, and support for
simultaneous DNA and RNA analysis, NGLIS enhances data accuracy,
traceability, and operational efficiency in clinical genomic testing.
Key Features
System Integration
EMR integration for test orders and results
Connectivity with clinical information databases
Integration with research data systems
Management of data from external and partner laboratories
Process Management
SOP-based standardized workflows
Experimental data and audit trail management
QC-based analytical instrument management
Web-based operational environment
Data Operations
Task-oriented, user-friendly UI/UX
Clinical information–linked knowledge base
Customizable genomic filtering and test data linkage
Rule-based clinical comments
Analytics & Reporting
Configurable categories and metrics
Analytics across test data, variants, actionable insights, reports, and quality indicators
Working Process
-
01EMR

NGS Test Order
02ORDER
Order Registration
03QC
Lab Data
Management
04SEQUENCING
Sequencer
Management
05ANALYSIS
Automated BI
Analysis Pipeline
06BI REVIEW
Analysis
Results Review
07CLINICAL REVIEW
Final Clinical
Review
08CLINICAL REPORT
Report
Generation
09EMR
Result Delivery
System Architecture

G-Hub is NGeneBio’s next-generation precision medicine platform,
designed to integrate its core data technologies and to be continuously
advanced in a phased and systematic manner.
G-Hub System
G-Hub transforms fragmented multimodal data from hospitals and
research environments into actionable insights.
By establishing meaningful connections across diverse data sources, it enables
the discovery of clinical value and supports healthcare professionals and researchers
in making accurate, evidence-based decisions through an advanced integrated platform.

Key Features
- Multimodal Data Library
-
Integrates and standardizes fragmented medical data,
including genomics (NGS), clinical data (EMR), and pathology,
into internationally compliant, AI-ready formats
to build high-quality multimodal datasets.
- AI-Driven Clinical Decision Support(CDSS)
-
Provides biomarker-informed treatment strategies and
supporting evidence through real-world data (RWD) analytics
to enable precision medicine–driven clinical decision-making.

- Biomarker-Based Intelligent Clinical Matching
-
Automatically matches patients to relevant clinical studies
by comparing individual genetic variants with global
clinical trial data in real time.
- RWE-Based Research Analytics
-
Visualizes and analyzes large-scale real-world evidence (RWE)
from clinical practice to support drug target discovery and
therapeutic candidate validation.
G-Hub Data Circulation Model
A continuous feedback loop connecting clinical data, AI insights, and research.
-
01
Standardization and Assetization of Medical Data
Standardizes and integrates multimodal medical data
to build scalable data assets applicable to diagnostics,
treatment, and drug discovery.Provides a platform that enables pharmaceutical companies,
research institutions, and hospitals to access and
utilize standardized data assets in real time. -

Expected Effects
-
04
Enhanced Precision Medicine–Driven Decision-Making
Supports clinicians in developing patient-specific diagnostic
strategies based on AI-generated clinical evidenceDelivers integrated genomic and clinical analysis reports
to enable evidence-based clinical decision-making
-
02
Optimized Clinical Trial Matching
Reduces time required to identify and match eligible patients
through real-time comparison of biomarkers and global clinical trial dataImproves research visibility, offering patients new treatment options
and enhancing protocol efficiency for researchers -
03
RWE-Driven Data Flywheel for Precision Medicine
Integrates real-world evidence (RWE) into drug R&D pipelines
to reduce trial-and-error and improve analytical qualityContinuously enhances data accuracy and precision
by incorporating real-world prognosis data and clinical feedback


