A Novel Machine Learning-enhanced Microfluidic CircRNAs Detection Platform for Breast Cancer Precision Diagnosis DOI
Lei Mou, Xinyu Zhang,

Zixin Lin

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: May 7, 2025

Abstract The discovery and favourable detection of breast cancer biomarkers are significant for diagnosis. Here, we show that hsa_circ_044235 hsa_circ_000250 effective Moreover, present an integrated electrochemical microfluidic circRNAs platform (ECMCDP) combined gold platinum nanoparticles (AuPts)-modified screen-printed electrodes (SPEs), catalytic hairpin assembly (CHA), chip a customed low-power electronic system simultaneous two cancer-associated circRNAs. limit (LOD) were 0.12 fM 0.1 fM, the diagnostic accuracy 92.50% 88.75% in clinical blood samples, respectively. was validated using paired pre-/post-operative tissue samples. Combined with five machine learning-based models, ensemble diagnosis model achieved high 93.75%. This work aims to identify novel establish innovative improve support prognosis assessment.

Language: Английский

Integrating machine learning and genetic evidence to uncover novel gene biomarkers for colorectal cancer diagnosis DOI Creative Commons
Li Zhou, Lihua Yu,

Mingjing Liao

et al.

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: May 6, 2025

Language: Английский

Citations

0
A Novel Machine Learning-enhanced Microfluidic CircRNAs Detection Platform for Breast Cancer Precision Diagnosis DOI
Lei Mou, Xinyu Zhang,

Zixin Lin

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: May 7, 2025

Abstract The discovery and favourable detection of breast cancer biomarkers are significant for diagnosis. Here, we show that hsa_circ_044235 hsa_circ_000250 effective Moreover, present an integrated electrochemical microfluidic circRNAs platform (ECMCDP) combined gold platinum nanoparticles (AuPts)-modified screen-printed electrodes (SPEs), catalytic hairpin assembly (CHA), chip a customed low-power electronic system simultaneous two cancer-associated circRNAs. limit (LOD) were 0.12 fM 0.1 fM, the diagnostic accuracy 92.50% 88.75% in clinical blood samples, respectively. was validated using paired pre-/post-operative tissue samples. Combined with five machine learning-based models, ensemble diagnosis model achieved high 93.75%. This work aims to identify novel establish innovative improve support prognosis assessment.

Language: Английский

Citations

0