Company: LNAI
Filing Date: 2025-05-15
Form Type: 10-Q
Source: 0001731122-25-000765
Chunk: 144

Company: Lunai Bioworks Inc.
Filing Date: 2025-05-15
Form: 10-Q
Item: Part I, Item 2
Chunk 144
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 across a spectrum of panels, which is supported by Renovaro Cube’s AI-guided panel mining, a proprietary combinatorial optimization technique used by Renovaro Cube’s AI technology. This approach, coupled with the capacity to explore numerous panels, significantly enhances the likelihood of discovering panels that align with specific metric criteria, such as sensitivity, specificity, precision, and recall and allows for tailoring criteria to align with clients’ unique needs, such as the number of biomarkers included per panel, or the inclusion of biomarkers associated with the expression of specific genes. The performance of the top-tier panels is further fine-tuned through the application of machine learning models. Subsequently, the efficacy of these biomarker panels in detecting cancer is validated through independent data sets.

    ●
    Phase III of the workflow involves Renovaro Cube’s collaboration with its clinical partners to validate the performance of the biomarker panels. Through this collaboration, Renovaro Cube can confirm the utility and accuracy of its biomarker panels in real-world clinical contexts. 

AI-Assisted Diagnostics

The process of biomarker discovery
facilitated by Renovaro Cube’s AI technology has yielded a set of data that enables scrutiny of the genomic distinctions and commonalities
inherent in diverse cancer types. This data set can support the diagnosis of cancers when their type or origin remains unidentified.

In addition to this role in biomarker
discovery and the development of diagnostic tests, Renovaro Cube’s AI technology also integrates AI-guided molecular profiling of
patient samples and furnishes diagnostic patient reports. These diagnostic reports reflect the outcomes of molecular profiling, coupled
with interpretations provided by Renovaro Cube’s team, to facilitate the process of cancer diagnostics by a qualified healthcare
provider, who can consider these reports in the context of a patient’s medical history, clinical signs, and symptoms, among other
factors.

Quality Control Process

Renovaro Cube undertakes post-processing
of data generated from sequence and arrays to ensure accurate and meaningful results. These post-processing steps for omic data include:

    1.
    Quality Control: Quality control is performed to assess the overall data quality and to identify any technical issues or anomalies.

    2.
    Normalization: arrays can introduce various sources of technical variation, such as batch effects, intensity variations, and probe-specific biases.

    3.
    Quality Filtering: After genotype calling, additional quality filtering may be performed to remove low-quality SNPs based on criteria like call rates, minor allele frequency, Hardy