Scalable Signature-Based Molecular Diagnostics Through On-chip Biomarker Profiling Coupled with Machine Learning

Area of Science:

• Biomedical Engineering
• Molecular Diagnostics
• Biomarker Discovery

Background:

• Traditional molecular diagnostics use single markers, limiting accuracy.
• Advances in on-chip screening and data analytics enable new diagnostic approaches.
• Signature-based diagnostics leverage multiple biomarkers for improved performance.

Purpose of the Study:

• To review signature-based diagnostics utilizing microfluidic and micro-/nano-technologies.
• To focus on device design, data analysis pipelines, and methodologies.
• To identify literature gaps and future directions in molecular diagnostics.

Main Methods:

• Review of current literature on microfluidic and micro-/nano-technologies for biomarker screening.
• Analysis of data handling and analytics approaches, including machine learning and image processing.
• Exploration of device design principles for signature-based diagnostics.

Main Results:

• Signature-based diagnostics demonstrate improved diagnostic accuracy and specificity.
• Microfluidic platforms are key enablers for high-throughput biomarker screening.
• Advanced data analytics, including machine learning, are crucial for interpreting complex biomarker signatures.

Conclusions:

• Signature-based diagnostics represent a significant advancement in molecular diagnostics.
• Further development in device integration and sophisticated data analysis is needed.
• Addressing current literature gaps will accelerate the development of next-generation diagnostic technologies.