BioMark Diagnostics Announces Breakthrough Publication Reinforcing Leadership in AI-Powered Metabolomics for Oncology
BioMark Diagnostics (BMKDF) has announced a breakthrough publication showcasing their novel Graph Neural Network (M-GNN) model for early lung cancer detection. The research, published in the International Journal of Molecular Sciences, demonstrates how the company's AI-powered approach enhances cancer diagnosis by analyzing metabolomics data alongside patient demographics and metabolic pathway information.
The study represents a collaboration between BioMark's scientific team, Harrisburg University of Science and Technology, and St. Boniface Hospital Research Centre. The M-GNN framework leverages advanced AI to interpret complex biological interactions, offering a scalable and interpretable tool for precision oncology that could be applied to BioMark's existing assays for lung, breast, and neuroendocrine cancers.
BioMark Diagnostics (BMKDF) ha annunciato una pubblicazione innovativa che presenta il loro nuovo modello di Rete Neurale a Grafi (M-GNN) per la diagnosi precoce del cancro ai polmoni. La ricerca, pubblicata sull'International Journal of Molecular Sciences, mostra come l'approccio basato sull'intelligenza artificiale dell'azienda migliori la diagnosi del cancro analizzando dati metabolomici insieme a informazioni demografiche dei pazienti e ai percorsi metabolici.
Lo studio è frutto di una collaborazione tra il team scientifico di BioMark, la Harrisburg University of Science and Technology e il St. Boniface Hospital Research Centre. Il framework M-GNN utilizza un'intelligenza artificiale avanzata per interpretare complesse interazioni biologiche, offrendo uno strumento scalabile e interpretabile per l'oncologia di precisione che potrebbe essere applicato agli attuali test di BioMark per i tumori polmonari, al seno e neuroendocrini.
BioMark Diagnostics (BMKDF) ha anunciado una publicación innovadora que presenta su nuevo modelo de Red Neuronal de Grafos (M-GNN) para la detección temprana del cáncer de pulmón. La investigación, publicada en el International Journal of Molecular Sciences, demuestra cómo el enfoque impulsado por IA de la compañía mejora el diagnóstico del cáncer analizando datos metabolómicos junto con la demografía del paciente y la información sobre vías metabólicas.
El estudio es el resultado de una colaboración entre el equipo científico de BioMark, la Harrisburg University of Science and Technology y el St. Boniface Hospital Research Centre. El marco M-GNN aprovecha la IA avanzada para interpretar complejas interacciones biológicas, ofreciendo una herramienta escalable e interpretable para la oncología de precisión que podría aplicarse a los ensayos existentes de BioMark para cánceres de pulmón, mama y neuroendocrinos.
BioMark Diagnostics (BMKDF)가 조기 폐암 진단을 위한 새로운 그래프 신경망(M-GNN) 모델을 소개하는 획기적인 논문을 발표했습니다. 이 연구는 International Journal of Molecular Sciences에 게재되었으며, 회사의 AI 기반 접근법이 환자의 인구통계학적 정보와 대사 경로 정보를 포함한 대사체학 데이터를 분석하여 암 진단을 향상시키는 방법을 보여줍니다.
이 연구는 BioMark의 과학 팀, 해리스버그 과학기술대학교, 세인트 보니파스 병원 연구 센터 간의 협력으로 이루어졌습니다. M-GNN 프레임워크는 복잡한 생물학적 상호작용을 해석하기 위해 첨단 AI를 활용하며, BioMark의 기존 폐암, 유방암 및 신경내분비암 검사에 적용할 수 있는 확장 가능하고 해석 가능한 정밀 종양학 도구를 제공합니다.
BioMark Diagnostics (BMKDF) a annoncé une publication révolutionnaire présentant leur nouveau modèle de Réseau de Neurones Graphes (M-GNN) pour la détection précoce du cancer du poumon. La recherche, publiée dans l'International Journal of Molecular Sciences, démontre comment l'approche alimentée par l'IA de la société améliore le diagnostic du cancer en analysant les données métabolomiques ainsi que les données démographiques des patients et les informations sur les voies métaboliques.
Cette étude est le fruit d'une collaboration entre l'équipe scientifique de BioMark, l'Université des Sciences et Technologies de Harrisburg et le Centre de Recherche de l'Hôpital St. Boniface. Le cadre M-GNN utilise une IA avancée pour interpréter les interactions biologiques complexes, offrant un outil évolutif et interprétable pour l'oncologie de précision qui pourrait être appliqué aux tests existants de BioMark pour les cancers du poumon, du sein et neuroendocriniens.
BioMark Diagnostics (BMKDF) hat eine bahnbrechende Veröffentlichung vorgestellt, die ihr neuartiges Graph Neural Network (M-GNN) Modell zur Früherkennung von Lungenkrebs präsentiert. Die Forschung, veröffentlicht im International Journal of Molecular Sciences, zeigt, wie der KI-gestützte Ansatz des Unternehmens die Krebsdiagnose verbessert, indem metabolomische Daten zusammen mit Patientendemografie und Informationen zu Stoffwechselwegen analysiert werden.
Die Studie ist das Ergebnis einer Zusammenarbeit zwischen dem wissenschaftlichen Team von BioMark, der Harrisburg University of Science and Technology und dem St. Boniface Hospital Research Centre. Das M-GNN-Framework nutzt fortschrittliche KI, um komplexe biologische Interaktionen zu interpretieren, und bietet ein skalierbares und interpretierbares Werkzeug für die Präzisionsonkologie, das auf BioMarks bestehende Tests für Lungen-, Brust- und neuroendokrine Krebserkrankungen angewendet werden könnte.
- Development of innovative M-GNN framework for enhanced lung cancer detection
- Potential application across multiple cancer types (lung, breast, and neuroendocrine)
- Successful collaboration with prestigious research institutions
- Framework shows promise for expansion into treatment response monitoring and therapeutic target discovery
- Further validation on larger, diverse real-world datasets still required for clinical translation
- Technology is still in research phase with no immediate commercialization timeline
Novel Graph Neural Network (N-GNN) Model Achieves Superior Accuracy in Early Lung Cancer Detection, Paving the Way for Enhanced Diagnostic Capabilities.
Vancouver, British Columbia--(Newsfile Corp. - May 20, 2025) - BioMark Diagnostics Inc. (CSE: BUX) (FSE: 20B) (OTCQB: BMKDF) ("BioMark"), a leader in developing liquid biopsy tests for early cancer detection, today announced the publication of a significant study exploring an artificial intelligence (AI) approach using novel graph neural networks (GNNs) that enhances the potential for early lung cancer diagnosis by modeling the complex web of metabolic pathways in cancer. The research paper, titled "M-GNN: A Graph Neural Network Framework for Lung Cancer Detection Using Metabolomics and Heterogeneous Graph Modeling," was accepted for publication in the Special Issue of International Journal of Molecular Sciences on Machine Learning in Bioinformatics and Biomedicine demonstrates how BioMark's investments in AI and machine learning are supporting current studies, boosting its cancer diagnostic platform and opening new frontiers in metabolomics.
The research, a collaboration between BioMark's scientific team, Harrisburg University of Science and Technology, and St. Boniface Hospital Research Centre & Asper Clinical Research Centre, introduces the M-GNN framework. This innovative model leverages graph neural networks to interpret complex biological interactions by analyzing metabolomics data alongside patient demographics and established metabolic pathway information.
"Recent advancements in GNNs have been proven effective in modeling relational data, making them ideal for capturing complex interactions within biological systems, such as those between patients' clinical data, blood metabolites, metabolic function, and disease pathways. GNNs have been applied to multi-omics data for cancer prognosis and subtype classification, including lung cancer. However, their application in metabolomics-driven early detection remains largely unexplored, even with the enriched relational context provided by databases like the Human Metabolome Database (HMDB)," says Jean-François Haince, PhD, BioMark's CSO.
This research signifies a pivotal advancement in the field of metabolomics and underscores BioMark's commitment to investing in AI-driven diagnostics. The M-GNN framework offers a scalable and interpretable tool for precision oncology, which can be used to further refine BioMark's existing assays for lung, breast, and neuroendocrine cancers and pave the way for new diagnostic and prognostic tools.
"Lung cancer remains a devastating disease where early detection is paramount for improving patient survival," said Rashid Bux, CEO of BioMark Diagnostics. "This publication showcases the power of integrating sophisticated AI, like graph neural networks, with our deep expertise in metabolomics. The M-GNN framework's ability to model intricate biological relationships represents a major step forward in our mission to deliver highly accurate and accessible early cancer detection solutions. We are immensely proud of our team of collaborators at Harrisburg University and St. Boniface Hospital Research Centre & Asper Clinical Research for this achievement."
While the M-GNN framework shows immense promise, further validation on larger, diverse real-world datasets will be important for clinical translation. The company is actively exploring pathways to integrate these advanced AI methodologies into its product development pipeline.
"By incorporating advanced AI like GNNs, BioMark is not just improving diagnostic accuracy; we are building a foundation for a new generation of precision oncology tools," added Mr. Bux. "This technology has the potential to expand beyond initial detection to areas like treatment response monitoring and the discovery of new therapeutic targets, solidifying our position at the forefront of AI-driven metabolomics."
The full publication can be accessed on the International Journal of Molecular Sciences website at: https://www.mdpi.com/1422-0067/26/10/4655
About BioMark Diagnostics Inc.
BioMark Diagnostics Inc. is a leading developer of liquid biopsy tests for the early detection of cancer that leverages the power of metabolomics and machine learning algorithms. The company's proprietary technology utilizes a simple blood draw to detect the presence of cancer-associated biomarkers, enabling earlier diagnosis and improved patient outcomes. The technology can also be used for measuring response to treatment and potentially for serial monitoring of cancer survivors. BioMark Diagnostics Inc. is committed to developing innovative and accessible diagnostic solutions to address unmet medical needs in oncology
Further information about BioMark Diagnostic Inc. is available under its profile on the SEDAR+ website www.sedarplus.ca and the CSE website https://thecse.com/.
For further information on BioMark Diagnostic Inc., please contact:
Rashid Ahmed Bux
President & CEO
BioMark Diagnostics Inc.
Tel. 604-370-0779
Email: info@biomarkdiagnostics.com
Forward-Looking Information:
This press release may include forward-looking information within the meaning of Canadian securities legislation, concerning the business of BioMark Diagnostics Inc. Forward-Looking information is based on certain key expectations and assumptions made by the management of the company. Although it believes that the expectations and assumptions on which such forward-looking information is based are reasonable, undue reliance should not be placed on the forward-looking information because BioMark Diagnostics Inc. can give no assurance that they will prove to be correct. Forward-Looking statements contained in this press release are made as of the date of this press release. BioMark Diagnostics Inc. disclaims any intent or obligation to update publicly any forward-looking information, whether as a result of new information, future events, or results or otherwise, other than as required by applicable securities laws.
The CSE has not reviewed, approved, or disapproved of the content of this press release.
To view the source version of this press release, please visit https://www.newsfilecorp.com/release/252444
FAQ
What is BioMark's (BMKDF) new M-GNN technology for cancer detection?
How does BioMark's (BMKDF) AI technology improve cancer detection?
What types of cancer can BioMark's (BMKDF) M-GNN framework detect?
What are the next steps for BioMark's (BMKDF) M-GNN technology?
Who are BioMark's (BMKDF) research partners for the M-GNN technology?
