BioMark Diagnostics Inc. (CNSX: BUX) (FSE: 20B) (OTCMKTS: BMKDF) said this week that its long running push to combine artificial intelligence with cancer diagnostics reached a major scientific milestone.
The Vancouver based company confirmed on Tuesday that a peer reviewed oncology journal accepted its lung cancer research for publication. The paper appears in Frontiers in Oncology, a leading international journal focused on cancer research and clinical translation.
The study details how machine learning models can detect lung cancer using blood based metabolic signals. Additionally, the work validates years of BioMark’s investment in artificial intelligence infrastructure and data science expertise.
The research centers on liquid biopsy technology, which analyzes blood samples instead of invasive tissue biopsies. This approach aims to detect cancer earlier, when treatment outcomes are typically stronger.
BioMark’s accepted paper describes a predictive model built on metabolomic pathway data rather than single biomarkers. Consequently, the model evaluates how groups of related metabolites behave together inside the body.
The company developed the model through a collaboration with Harrisburg University of Science and Technology. Further, the research team worked under the leadership of Dr. Maria Vaida, a specialist in data driven biomedical science.
The study draws data from the Human Metabolome Database, a widely used reference for human metabolic processes. Additionally, the researchers applied explainability tools to better understand how the model reaches its predictions.
One of those tools, known as SHAP analysis, helps show which metabolic pathways influence the results most strongly. This transparency allows scientists to link the model’s output to known biological mechanisms.
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Work shows AI can produce clinically meaningful insights
The paper explains that certain pathways tied to nutrient use and tumor growth play a key role in detection. Meanwhile, the researchers emphasize that pathway level analysis reduces noise seen in single marker approaches.
BioMark said the findings strengthen confidence in its strategy to integrate artificial intelligence into diagnostics. Furthermore, the acceptance signals that external reviewers found the methodology scientifically sound.
Chief executive Rashid Ahmed Bux described the publication as third-party validation of prior technology spending. However, he framed the achievement as a scientific milestone rather than a commercial endpoint.
He said the work shows artificial intelligence can produce clinically meaningful insights when paired with metabolomics. Additionally, he noted that healthcare investors increasingly demand real-world evidence for AI claims.
The timing coincides with heightened interest in artificial intelligence across healthcare markets.
This week, industry leaders gathered at the J.P. Morgan Healthcare Conference to discuss AI-driven innovation. BioMark positioned the publication as evidence that its technology extends beyond conceptual research. Consequently, the company believes it now operates on a peer-reviewed scientific foundation.
The collaboration with Harrisburg University helped translate complex data science into medically relevant models. In addition, the partnership ensured the research met academic standards required for journal acceptance.
BioMark said it plans to make the full paper available through its website later this month. Subsequently, the company expects researchers and clinicians to review the findings in greater detail. The study focuses on lung cancer, one of the world’s leading causes of cancer-related death.
Early detection remains a central challenge, particularly for patients without obvious symptoms.
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AI advances allow researchers further analysis powers
Liquid biopsy technologies aim to address that gap using routine blood samples. Further, machine learning allows computers to detect subtle patterns humans might miss. BioMark continues to develop its platform at the intersection of oncology, metabolomics, and artificial intelligence. Meanwhile, the company positions peer-reviewed research as a prerequisite for future clinical adoption.
The publication also fits into a wider shift in how lung cancer is diagnosed, screened, and monitored.
Lung cancer remains one of the deadliest cancers largely because it is often found too late.
Consequently, researchers increasingly focus on tools that detect disease before symptoms appear.
Traditional imaging and tissue biopsies remain effective, however they can miss early-stage disease.
Additionally, those methods often involve invasive procedures that limit repeat testing.
This diagnostic gap has driven interest in blood-based testing, breath analysis, and data-driven screening tools. Further, advances in artificial intelligence now allow researchers to analyze complex biological signals at scale. As a result, early detection has become a central theme across oncology research and investment.
Several publicly traded companies already operate in this space with lung cancer-focused platforms.
Guardant Health Inc. (NASDAQ: GH) develops liquid biopsy tests designed to detect and monitor cancer using blood samples. Its GuardantINFORM and LUNAR programs target early-stage cancer detection, including lung cancer.
However, much of the most experimental work is occurring outside public markets. Smaller startups increasingly test unconventional biological signals and novel sampling methods.
One such company is Breath Diagnostics Inc., which focuses on non-invasive breath-based cancer detection. Its OneBreath technology analyzes volatile organic compounds exhaled by patients.
Further, the platform aims to identify metabolic patterns associated with lung cancer at early stages.
Breath Diagnostics positions the technology as a low-cost screening tool suitable for routine clinical use.
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