Company: LNAI
Filing Date: 2025-02-19
Form Type: 10-K/A
Source: 0001731122-25-000252
Chunk: 17

Company: Lunai Bioworks Inc.
Filing Date: 2025-02-19
Form: 10-K/A
Chunk 17
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 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. |

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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.

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 need for transparency, interpretability,
and accountability in AI applications.

In traditional machine learning
approaches, such as deep neural networks, the internal workings of the model can be complex and difficult to interpret. This lack of interpretability
poses challenges in critical domains where decisions have significant implications, such as healthcare.

Renovaro Cube believes that Explainable
AI is crucial for ensuring transparency, fairness, and accountability in AI systems. Renovaro Cube’s AI platform includes Explainable
AI by design. All data points, calculations and results are traceable, and all calculations are verifiable and reproducible with the same
result.

Disease prognosis is one of the
diagnostic capabilities of the Explainable AI feature of Renovaro Cube’s technology. Disease prognosis gives more insight for