ONCOfind AI ®
Multi-omics-based early diagnostic platform
ONCOfind AI® is an AI-powered analytics platform that integrates cancer-related multi-omics data to identify cancer-specific molecular signals and biomarker patterns.
By analyzing blood-based genomic, transcriptomic, proteomic, and metabolomic data, it provides molecular-level insights that can support primary cancer site prediction, early cancer detection, and post-treatment monitoring.
Carcinoma of Unknown Primary (CUP)
Cancer of unknown primary is metastatic cancer in which the primary site is difficult to identify. When the primary site is unknown, anticancer treatment is difficult and prognosis is poor, making primary-site prediction important.
Highest accuracy and cancer types
It predicts the largest number of cancer types with the highest accuracy among existing cancer-of-unknown-primary prediction models.
EARLY DETECTION & MONITORING
Integrated AI Across the Cancer Journey
By integrating blood-based multi-omics data with AI, ONCOfind AI® precisely analyzes molecular signal changes associated with cancer development, treatment response, and recurrence risk.
Multi-omics-based early diagnostic platform
ONCOfind AI® integrates blood-based multi-omics data with AI to identify cancer-specific biomarker patterns and molecular signal changes.
This enables molecular-level analysis of cancer-related changes before imaging-based diagnosis, as well as treatment response and recurrence-related signals.
Cancer-specific multi-omics model and diagnostic accuracy
ONCOfind AI® learns from cancer-type-specific multi-omics data to analyze composite biomarker patterns that distinguish cancer-related signals from normal controls.
Its AI/ML model integrates molecular signals that are difficult to capture with single-omics data alone, supporting the evaluation of early detection and monitoring potential.
KEY APPLICATION AREAS
Three Core Analytical Applications
ONCOfind AI® is an AI-based diagnostic solution that expands from primary-site prediction to early cancer diagnosis and post-treatment monitoring.
Step 01
Primary Site Prediction
Supports prediction of the primary cancer site in CUP patients by learning from cancer-type-specific multi-omics data.
Step 02
Early Cancer Diagnosis
Analyzes cancer-related molecular signals through AI/ML models and evaluates the potential for early detection across multiple cancer types.
Step 03
Post-treatment Monitoring
Monitors molecular signal changes associated with minimal residual disease and recurrence risk after treatment.
TECHNOLOGY & ACCURACY
Cancer-specific multi-omics model and diagnostic accuracy
By training on thousands of cancer-type-specific integrated multi-omics datasets, we identified composite biomarker panels showing significant differences from normal controls.
Multi-omics-based early diagnostic platform
By integrating and analyzing tens of thousands of multi-layered data points in blood, including genomics, transcriptomics, proteins, and metabolites, AI enables early cancer detection. It captures complex biological signal changes in cancer cells at stages earlier than imaging medicine and tracks early diagnosis and signs of recurrence at the molecular level.
Cancer-specific multi-omics model and diagnostic accuracy
By training on thousands of cancer-type-specific integrated multi-omics datasets, we identified composite biomarker panels showing significant differences from normal controls. AI/ML models calculate cancer risk with dramatically higher accuracy than single-omics approaches and serve as a core solution for molecular-level early diagnosis and recurrence monitoring.
Together with Oncocross, we create new opportunities in drug discovery, diagnostics,
and data-driven expansion.
Let's Build the Next Possibility in Precision Medicine