BioMark Diagnostics Announces Publication of Peer-Reviewed Validation of Machine Learning Models for Lung Cancer Detection in Frontiers in Oncology

About this update from Biomark Diagnostics, Inc.

Advancing Superior Accuracy in Early Lung Cancer Detection Using Selective Metabolic Pathways and Data Enrichment for Enhanced Diagnostic Capabilities Vancouver, British Columbia--(Newsfile Corp. - January 15, 2026) - BioMark Diagnostics Inc. (CSE: BUX) (FSE: 20B) (OTCQB: BMKDF) ("BioMark"), a leading developer of liquid biopsy technologies for early cancer detection, is pleased to announce that its long-term investment in integrating artificial intelligence and machine learning into metabolomic profiling has achieved a major milestone. As the global healthcare investment community is focused on the transformative power of AI at the J.P. Morgan Healthcare Conference, BioMark has received this week notification that its research regarding a machine learning-driven predictive model for lung cancer detection has been accepted for publication in the prestigious, peer-reviewed journal Frontiers in Oncology. The accepted article, titled "Translational impact of machine learning-driven predictive modeling with pathway-based plasma metabolomic biomarkers for lung cancer detection," represents a successful realization of BioMark's strategy to utilize advanced computational tools to enhance diagnostic precision. This achievement validates the company's past capital allocation toward sophisticated AI infrastructure and confirms the scientific rigor of its metabolic pathway analysis. By moving beyond traditional biomarker identification and utilizing pathway-level data, BioMark has demonstrated a more comprehensive way to interpret the metabolic signatures of early-stage lung cancer. This research was made possible through a strategic collaboration with Dr. Maria Vaida and her team at the Harrisburg University of Science and Technology. BioMark is proud to highlight that the work led by Dr. Vaida has been recognized for its high quality and its significant contribution to the field of oncology. The publication describes the use of pathway-based features from the Human Metabolome Database (HMDB) and explores the mechanistic drivers of cancer through interpretability tools such as SHAP analysis. This methodological approach allows for a deeper understanding of how specific metabolic pathways, such as those involved in nutrient processing and tumor growth, contribute to diagnostic models. The partnership with Harrisburg University has been in...

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