MSAID introduces the MSAID Platform powered by CHIMERYS 4. The All-in-one Solution for Proteomics

ANAHEIM, Calif. - June 28, 2024 - PRLog -- MSAID, a leader in artificial intelligence for proteomics, announces the launch of a highly scalable, fully automatable, cloud-based SaaS platform designed to revolutionize the proteomic data workflow. The MSAID Platform handles everything from raw data storage and processing to systematic result storage and easy data interpretation, serving as a catalyst for seamless data management. This comprehensive pipeline streamlines the entire workflow, transforming raw data into actionable insights, all accessible online to researchers from academia and industry through a convenient subscription model.

MSAID is showcasing the new software solution during the 72nd American Society for Mass Spectrometry (ASMS) Conference on Mass Spectrometry and Allied Topics, being held June 2-6, in Anaheim, California.

"The MSAID Platform for proteomics represents a groundbreaking advancement in proteomics software. By eliminating the need for local software and on-premises processing, this platform propels proteomics data analysis into a cloud-based future, ensuring faster, more efficient, and scalable solutions for researchers worldwide," said Martin Frejno, CEO of MSAID.

The platform also features API integration for easy automation and interconnectivity with other cloud-based pipelines already in place. Tiered subscription options are available to suit different user needs.

For more information on CHIMERYS 4 and the MSAID Platform, please visit www.msaid.de or join us at ASMS 2024 at booth #200.

About MSAID GmbH

MSAID - the leader in artificial intelligence for proteomics - transforms the way scientists analyze proteomic data. The company was founded by an interdisciplinary team of scientists with the vision to provide better computational solutions to the field of proteomics. Our approach is to replace current algorithms with powerful, artificial intelligence-fueled software solutions, thereby paving the way for a smarter, deeper and more reliable way of interrogating proteomics data.

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