MSAID specializes in the development of software for the analysis of proteomics data utilizing the latest advances in deep learning. We make our innovative services and solutions easily and readily available, thereby boosting the use of machine learning in the field of proteomics. As part of our contract research business, we offer customized data analysis, development of customized machine learning solutions and consulting.
Powered by vast amounts of data, MSAID develops deep learning models for bottom-up proteomics and associated software frameworks for a smarter, more comprehensive and more reliable identification of peptides and proteins.
MSAID offers a variety of services that can be tailored to adress your needs. Talk to us to learn how our customized solutions can advance your data analysis pipelines. Examples:
- Custom deep learning models: our generic deep learning framework provides the flexibility to expand our prediction capabilities and customize them to your needs, instruments, and infrastructure.
- Custom in-silico spectral libraries: generating large spectral libraries is cumbersome, expensive, and rarely yields comprehensive and transferable results. Our deep learning models can generate spectral libraries of full proteomes for you. They are calibrated to your mass spectrometer setup and can be directly applied to the analysis of data dependent (DDA), data independent (DIA) and targeted proteomics (SRM/MRM/PRM) data.
- Deep learning-based data analysis: we have developed next-generation data analysis services that reliably identify up to three times more peptide-spectrum-matches than classical workflows. This increases the confidence in valuable biomarker candidates from new and existing data by at least one order of magnitude and increases productivity of your expensive LC-MS/MS equipment
- Consulting: with our long-standing expertise in proteomics and proteomic data analysis, we are the perfect partner to test, evaluate and improve your proteomics data workflows. Together, we dig deeper into your samples, evaluate your data processing pipelines, or decide how to best equip your laboratory with the next generation of computing infrastructure.