Company: LNAI
Filing Date: 2025-02-19
Form Type: 10-Q
Source: 0001731122-25-000258
Chunk: 107

Company: Lunai Bioworks Inc.
Filing Date: 2025-02-19
Form: 10-Q
Item: Part I, Item 8
Chunk 107
---
 and velocity of its organization, including the development of the AI platform and the opportunities to deploy
the AI platform for research perspective and ultimately for clinical practice and into clinical trials.

In addition, Renovaro Cube intends
to build out its infrastructure by leasing space for storage, networking and hosting facilities.

Target Market

Renovaro Cube’s intended
customers will be hospitals, clinics, insurance companies, pharmaceutical companies, biotech companies, research centers, physicians and
individual patients.

Renovaro Cube aims to utilize its
AI technology to commercialize products and test kits for healthcare providers, hospitals, clinics and doctors that will expedite diagnosis
and the selection of appropriate treatment for various types of cancer. Renovaro Cube intends to differentiate its products based on the
following factors:

    ●
    Proprietary and unique panel mining algorithms to create multiple biomarker stratifications per cancer;

    ●
    Explainable AI, offering traceability between the prediction and the exact biomarkers, panels and genes;

    ●
    Differential diagnosis, inclusion and exclusion of cancer types based on facts; and

    ●
    Precision diagnosis, with a high accuracy percentage with machine-learning tuning.

The multi-omic design of Renovaro
Cube’s AI platform enables the use of different molecular layers, such as epigenomics, transcriptomics, and metabolomics, together
with genomics and clinical data.

Panel Mining 

The unique panel mining technique
in Renovaro Cube’s technology repeatedly investigates genes to identify relevant biomarkers. The proprietary technique in Renovaro
Cube’s technology not only searches for individual biomarkers, but also integrates validated panels for different cancer types into
the “RenovaroCube” machine learning library. This process enables precision diagnosis, by including one cancer and excluding
others based on statistically, scientifically and clinically validated machine-learning panels.

30

Panel mining is designed to combine
biomarkers into panels in such a way that the final panel meets:

    ●
    performance metric criteria;

    ●
    technical criteria, such as a minimum or maximum number of biomarkers for the selected assay;

    ●
    biological criteria, non-annotated genes inclusion; and

    ●
    stratification criteria.

Explainable AI

The term “Explainable AI”
refers to the ability of an AI system or model to provide human-understandable explanations for its decision-making process or predictions.
This feature aims to bridge the gap between the “black box” nature of many AI algorithms and the