Providing advanced data analyitic and biomarker discovery solutions for clients in pharma and biotech.
Raw proteomic data collected from LCMS experiments can be difficult to access and process, particularly with common data tools such as R and Python. We provide services to convert your instrument specific data into a universal format enabling open access and efficient data processing across most all programming languages.
Data manipulation and modeling with R, Python and Java. Cloud compute servers and data center engineering. Laboratory instrument operations and optimization.
Over 20 years combined industry experience in biomarker discovery. Advanced degrees in Biology, Analytical Chemistry and Informatics.
Multiple peer reviewed publications on LCMS and proteomics. Patents in clinical biomarkers. Developed CLIA LDT blood-based screening test for colorectal cancer.
Turning LCMS data into biological insight is essentially a well practiced operation, however inconsistent. Several tools exist to extract molecular information and re-construct protein sequences, but the processes vary from tool to tool. We provide a concise method for translating label-free proteomics into biological meaning which can be easily analyzed, searched and explored.
Patient stratification to laboratory work flow, SCBI has helped clients develop processes to ensure quality data is generated from the beginning.
With existing data SCBI can apply proprietary tools to reduce variability, expand biological coverage and filter outlier data prior to statistical analysis.
Using advanced machine learning techniques and custom discovery pipelines, SCBI can find the signal hidden among the biological noise.
Proteomic biomarkers have the power to assess one’s current biological state, unlike genomics which can only provide a static snapshot of what could be - butterfly or caterpillar. We apply machine learning solutions to large biological datasets to discover relevent markers for the current biological state.