Yasset Perez-Riverol

Yingying Sun, Jun A, Zhiwei Liu et al.

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis; improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and ultimately enabling AI-empowered virtual cells.

Yasset Perez-Riverol, Roberto Vera Alvarez

Parallel and distributed application design is a major area of interest in the domain of high performance scientific and industrial computing. Over the years, various approaches have been proposed to aid parallel program developers to modeling their applications. In this paper it will be used some concepts from agile development methodologies and Unified Modeling Language (UML) to modeling parallel and distributed applications. The UML-based approach of this paper describes through different artifacts and graphs the main flows of events in the development of parallel and high performance applications. Here, we presented three work flows to describe and to model our parallel program, Domain Model, Design and Modeling and Test. All these phases of the development software allow to programmers convert the requirements of the problem in a good and efficient solution.