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.

NGLIS Workflow

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

  • 01
    EMR
    NGS Test Order
    02
    ORDER
    Order Registration
    03
    QC
    Lab Data
    Management
    04
    SEQUENCING
    Sequencer
    Management
    05
    ANALYSIS
    Automated BI
    Analysis Pipeline
    06
    BI REVIEW
    Analysis
    Results Review
    07
    CLINICAL REVIEW
    Final Clinical
    Review
    08
    CLINICAL REPORT
    Report
    Generation
    09
    EMR
    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 evidence

    Delivers 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 data

    Improves 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 quality

    Continuously enhances data accuracy and precision
    by incorporating real-world prognosis data and clinical feedback