2 citations · 2 across the 5 of their papers we have counts for
5 papers
From Research Question to Scientific Workflow: Leveraging Agentic AI for Science Automation
Bartosz Balis, Michal Orzechowski, Piotr Kica +2
Scientific workflow systems automate execution -- scheduling, fault tolerance, resource management -- but not the semantic translation that precedes it. Scientists still manually c…
Accelerating Cloud-Based Transcriptomics: Performance Analysis and Optimization of the STAR Aligner Workflow
Piotr Kica, Sabina Lichołai, Michał Orzechowski +1
In this work, we explore the Transcriptomics Atlas pipeline adapted for cost-efficient and high-throughput computing in the cloud. We propose a scalable, cloud-native architecture…
Serverless Approach to Running Resource-Intensive STAR Aligner
Piotr Kica, Michał Orzechowski, Maciej Malawski
The application of serverless computing for alignment of RNA-sequences can improve many existing bioinformatics workflows by reducing operational costs and execution times. This wo…
Optimizing STAR Aligner for High Throughput Computing in the Cloud
Piotr Kica, Sabina Lichołai, Michał Orzechowski +1
We propose a scalable, cloud-native architecture designed for Transcriptomics Atlas Pipeline, using a resource-intensive STAR aligner and processing tens or hundreds of terabytes o…
The Common Workflow Scheduler Interface: Status Quo and Future Plans
Fabian Lehmann, Jonathan Bader, Lauritz Thamsen +1
Nowadays, many scientific workflows from different domains, such as Remote Sensing, Astronomy, and Bioinformatics, are executed on large computing infrastructures managed by resour…