Company: LNAI
Filing Date: 2025-11-14
Form Type: 10-Q
Source: 0001731122-25-001544
Chunk: 112

Company: Lunai Bioworks Inc.
Filing Date: 2025-11-14
Form: 10-Q
Item: Part I, Item 2
Chunk 112
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, to advance biomarker discovery, therapeutic development, and precision medicine. BioSymetrics has developed proprietary
machine learning pipelines that harmonize and analyze complex, heterogeneous datasets to uncover clinically actionable insights. These
insights are designed to support pharmaceutical research, early disease detection, and personalized treatment strategies.

BioSymetrics collaborates with
pharmaceutical companies, healthcare providers, and academic institutions to co-develop analytical tools and translational research programs.
Application areas of the BioSymetrics platform include: (i) multi-omics integration for target identification and validation; (ii) predictive
modeling of therapeutic efficacy and safety; (iii) clinical trial optimization, including patient stratification and response monitoring;
and (iv) experimental screening of gene and small molecule effects for the purpose of identifying novel drug targets and therapeutics.

The key to the BioSymetrics approach
is Contingent AI. In Contingent AI, any “settable” parameter for data processing, data integration, or feature selection is
permuted and the corresponding effects on the downstream predictive model measured. This process is similar in nature to hyperparameter
tuning in machine learning, however instead of optimizing only the machine learning model, the entire data science pipeline (including
model selection) is subject to optimization. We have applied this method extensively in drug discovery, producing marked improvements
in experiment interpretation and lead generation.

Another
major component of BioSymetrics’ platform is the Phenograph. The PhenographTM is BioSymetrics’ proprietary knowledge
graph. The purpose of the PhenographTM is to map human genes and phenotypes to those of model systems, allowing virtual
phenotypic screening, target nomination, and active learning feedback. The PhenographTM contains 5,856 diseases associated
with one or more phenotypes in humans, and 16,676 human genes with one or more orthologous zebrafish genes. Zebrafish genes are mapped
to phenotypic terms using over 300 individually-trained machine learning models, that fill in the gaps of known gene-phenotype associations.
Leveraging this platform, we can prioritize human genes on the basis of predicted experimental phenotype, reducing the number of required
experiments to produce human-informed in vivo disease models.

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Finally, one important component of the BioSymetrics platform is that it couples AI-based prediction
with experimental validation. BioSymetrics has designed deep learning-based computer vision software that automatically identifies and
characterizes organ systems relevant to neurological, cardiovascular, and muscle development