Smart Biomarker Discovery Platform: AI and ML Empowering Biomedical Research

A European biotechnology company specializing in developing tools that support diagnostics and biomedical research, collaborating with academic and clinical centers worldwide.

Researchers involved in biomarker discovery faced difficulties analyzing complex proteomic and metabolomic data. The process required advanced expertise in bioinformatics, artificial intelligence, and programming—skills many scientists lacked. The shortage of bioinformatics specialists further slowed the progress of medical research. The company needed a tool that could simplify mass spectrometry data analysis and leverage AI without requiring coding.

We designed intuitive software that uses artificial intelligence (AI) techniques and advanced machine learning (ML) models to identify strong disease biomarkers in proteomic and metabolomic data—without the need for coding. The solution includes: 

• An automated ML pipeline for data processing (alignment, denoising, background removal, peak extraction) 
• Machine learning algorithms for dimensionality reduction and feature selection based on stability 
• AI-supported statistical validation of selected feature significance 
• Predictive ML model building for classification and diagnosis 
• Integration with open protein databases 
• Compatibility with popular mass spectrometry data formats 
• Full automation of analytical processes using AI and ML, ensuring reproducible research analysis 

The project was divided into three key phases: 

1. Needs Analysis and Functional Design
   We began with literature reviews, interviews with potential users, and market analysis. This allowed us to accurately identify automation and AI/ML integration needs in daily scientific workflows.
   
2. System Development and Prototype Validation
   Using the gathered insights, we developed a prototype equipped with proprietary ML algorithms, tested for usability and analytical effectiveness. We simultaneously designed the system architecture and created custom algorithms for mass spectrometry data processing. Development was iterative, based on real data analysis and continuous user feedback.
   
3. Solution Implementation and User Personalization
   In the final phase, we focused on implementing dedicated AI/ML modules tailored to specific research institutions, customizing both features and user interface to meet individual scientific needs. 

The ML- and AI-powered platform has been implemented in leading research institutions in Japan, Poland, and Sweden, supporting scientific teams in their daily research activities. Its effectiveness has been validated through numerous scientific publications, increasing trust in the tool within academic and clinical communities. 

Thanks to its user-friendly interface and full automation, the platform significantly reduced the time required for data analysis and enabled researchers without programming experience to identify biomarkers. It also broadened access to advanced bioinformatics methods, ensured reproducibility and standardization of analyses, and supported further research and validation processes in medical diagnostics. 

Team successfully combined expertise in bioinformatics, software engineering, and UX to create a tool that is efficient, flexible, and researcher-friendly. Close collaboration with scientists and responsiveness to user needs throughout each phase of the project were also critical to its success.