R&D

DeepOmicsTM

AI-based customized analysis platform

Theragen Bio has its own DeepOmics™  platform, a unique genome analysis method, by combining more than a decade of
genome analysis experiences and research capabilities with artificial intelligence research capabilities.
The DeepOmics™ platform is an artificial intelligence platform that automatically executes the entire process of
disease classification (Molecular subtype classification in cancer), subtype specific biomarker discovery, therapeutic target discovery,
in silico drug screening, and in silico de novo drug design.

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DeepOmicsNeo™

DeepOmicsNeoTM identifies somatic cell mutations present in cancer tissues of patient
and predicts neoantigens for individual MHCs.

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Patient-specific
mutation identification
Prediction of
neopeitopes
 
Neoepitope
Vaccivation
 
Immune activation
by neoepitopes
 
Cancer cell
death
 

Unlike the existing methodology, which only considered the combination of MHC and new antigen, DeepOmicsNeo™ is a deep learning model that considers
East Asian-specific rare MHC as well asthe activity of T cells, showing superior performance compared to other methodologies.

DeepOmicsNeo™ Achievement

Innovative technology that improved immunogenicity
predictability to the highest level in the world

It is a groundbreaking artifical intelligence technology that predicts HLA restricted neoepitopes among mutations identified through sequencing cancer tissue. TheragenBio achieved three registered patents.

HLA ClassⅠ

HLA ClassⅠ

Patent 1. HLA ClassⅠ

A model that learned through deep learning the ‘binding’ between HLA and peptide, which the existing algorithms focused on, and the ‘immunogenicity’ that stimulates T cells after binding

Both aspects of "Immunogenicity" learned through deep learning
(① Immunogenicity based on amino acid sequence of the peptides
② Immunogenicity based on T-cell recognition of peptide-MHC complex)

HLA ClassⅡ

HLA ClassⅡ

Patent 2. HLA ClassⅡ

Predicting a neoantigen that binds to HLA Class II, where a combination of alpha and beta chains occurs, and deriving a synthetic long peptide neoantigen vaccine candidates reflecting both Class I and Class II

Inducing the activity of CD4+ T cells as well as CD8+ T cells in patients results in superior therapeutic efficacy

Rare HLA

Rare HLA
Solution

Patent 3. Rare HLA, Provide solutions for rate HLA types and varying length of neoepitopes

Applying modeling to overcome the lack of data for rare HLA alleles

Overcoming the problem of unstable predictions for East Asian HLA types

Superior efficacy of neoepitope vaccine designed with DeepOmicsNeo™

mRNA vaccine designed with DeepOmicsNeo™ showed superior efficacy over vaccine design by the global leader in B6F10 melanoma tumor model.

Rare HLA

DeepOmicsMarker™

DeepOmicsMarkerTM is a platform that automatically identifies disease subtypes and develops subtype classifiers.

Patients-classified biomarker

  • Patient Classification
  • Biomarker
  • CDx
Patients-classified biomarker
Classification of molecular subtypes
Automatic classification of molecular subtypes with different characteristics for the target disease.
Disease
transcriptomics
Major characteristics derived from transcriptional data of the target disease without normal control group.
Characteristics of molecular subtypes
Discovery of specific major genes and signatures for each molecular subtype based on the disease transcriptomics
Deep learning-based
classification model
Suggestion of deep learning classification model based on the discovered major signature

Compared to the existing molecular subtype classification method, DeepOmicsMarker™ provides finer classification with superior clinical applicability, which ultimately links to DeepOmicsNetwork™ and DeepOmicsTarget™ for automatic suggestion for targeted therapeutics.

Application

  • Patient classification of drug responses
  • Suggestion of major signatures and genes vaild for drug responses

Differentiation of the method
for input processing

DeepOmicsMarker™ utilizes Theragen Bio-specific data preprocessing that is completely different from the existing RNA-seq analysis (Patient No. 10-2385483). When cancer cell lines are analyzed using exisitng methods, many of them fail to map to their site of origin. When the same data are analyzed using DeepOmicsMarker™, mapping to the site of origin is much improved - suggesting the superiority of our analysis platform.

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